70
Post Doctoral Research Achievements - Review November 2006 August 2008 November 2006 August 2008 Kai Knoerzer 15/08/2008

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Post Doctoral Research Achievements- ReviewNovember 2006 ndash August 2008November 2006 ndash August 2008

Kai Knoerzer

15082008

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Competitive and sustainable business

EU Food Company (HPTS2 project)

Potential new project with EU Food Company

FFF (Drying of natural plant material)

CSIRO TFT (US wool wax recovery)

Aus and Int Veg Processor (US chips)

Aus Fruit Processor (US pears HPP peach)

BD (HPTS HP-T logger)

Building partnerships

Collaboration with FSA North Ryde

Niche Manufacturing FlagshipCSIRO

Minerals

Thermochron manufacturer

Sonosys GmbH

Others (eg institutes divisions etc) IFTNPD symposium proposal

Book ldquoMultiphysics Modelling of Emerging Technologiesrdquo

Contributions to FSArsquos KSIs

Relevant and excellent science

Peer-reviewed papers 5

Industry papers 3

Book chapters 3

Abstractsproceedings 15

Posters 7

Oral presentations 10

Internal 15+

Symposium and Book

IFT International Division website editor

People and culture

FSA-WB modelling team

HP-T process logger team (WBNR)

HPTS team (WBNR)

FFF processing team (NRWB)

Capability Development team (WBNR)

Supervision of students (STI3 PhD work

experience)

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Main projects

STI3 project ( 110653)

Initial HPTS modelling

Temperature mapping (35 L vessel)

Preliminary studies of HP-T process logger

EU Food Company HPTS2 project ( 112812) EU Food Company HPTS2 project ( 112812)

HPTS modelling

Temperature mapping (3 L vessel)

Carrier design

Compression heating of sauces

ITD value development

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 2: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Competitive and sustainable business

EU Food Company (HPTS2 project)

Potential new project with EU Food Company

FFF (Drying of natural plant material)

CSIRO TFT (US wool wax recovery)

Aus and Int Veg Processor (US chips)

Aus Fruit Processor (US pears HPP peach)

BD (HPTS HP-T logger)

Building partnerships

Collaboration with FSA North Ryde

Niche Manufacturing FlagshipCSIRO

Minerals

Thermochron manufacturer

Sonosys GmbH

Others (eg institutes divisions etc) IFTNPD symposium proposal

Book ldquoMultiphysics Modelling of Emerging Technologiesrdquo

Contributions to FSArsquos KSIs

Relevant and excellent science

Peer-reviewed papers 5

Industry papers 3

Book chapters 3

Abstractsproceedings 15

Posters 7

Oral presentations 10

Internal 15+

Symposium and Book

IFT International Division website editor

People and culture

FSA-WB modelling team

HP-T process logger team (WBNR)

HPTS team (WBNR)

FFF processing team (NRWB)

Capability Development team (WBNR)

Supervision of students (STI3 PhD work

experience)

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Main projects

STI3 project ( 110653)

Initial HPTS modelling

Temperature mapping (35 L vessel)

Preliminary studies of HP-T process logger

EU Food Company HPTS2 project ( 112812) EU Food Company HPTS2 project ( 112812)

HPTS modelling

Temperature mapping (3 L vessel)

Carrier design

Compression heating of sauces

ITD value development

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 3: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Competitive and sustainable business

EU Food Company (HPTS2 project)

Potential new project with EU Food Company

FFF (Drying of natural plant material)

CSIRO TFT (US wool wax recovery)

Aus and Int Veg Processor (US chips)

Aus Fruit Processor (US pears HPP peach)

BD (HPTS HP-T logger)

Building partnerships

Collaboration with FSA North Ryde

Niche Manufacturing FlagshipCSIRO

Minerals

Thermochron manufacturer

Sonosys GmbH

Others (eg institutes divisions etc) IFTNPD symposium proposal

Book ldquoMultiphysics Modelling of Emerging Technologiesrdquo

Contributions to FSArsquos KSIs

Relevant and excellent science

Peer-reviewed papers 5

Industry papers 3

Book chapters 3

Abstractsproceedings 15

Posters 7

Oral presentations 10

Internal 15+

Symposium and Book

IFT International Division website editor

People and culture

FSA-WB modelling team

HP-T process logger team (WBNR)

HPTS team (WBNR)

FFF processing team (NRWB)

Capability Development team (WBNR)

Supervision of students (STI3 PhD work

experience)

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Main projects

STI3 project ( 110653)

Initial HPTS modelling

Temperature mapping (35 L vessel)

Preliminary studies of HP-T process logger

EU Food Company HPTS2 project ( 112812) EU Food Company HPTS2 project ( 112812)

HPTS modelling

Temperature mapping (3 L vessel)

Carrier design

Compression heating of sauces

ITD value development

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 4: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Main projects

STI3 project ( 110653)

Initial HPTS modelling

Temperature mapping (35 L vessel)

Preliminary studies of HP-T process logger

EU Food Company HPTS2 project ( 112812) EU Food Company HPTS2 project ( 112812)

HPTS modelling

Temperature mapping (3 L vessel)

Carrier design

Compression heating of sauces

ITD value development

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 5: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Main projects

STI3 project ( 110653)

Initial HPTS modelling

Temperature mapping (35 L vessel)

Preliminary studies of HP-T process logger

EU Food Company HPTS2 project ( 112812) EU Food Company HPTS2 project ( 112812)

HPTS modelling

Temperature mapping (3 L vessel)

Carrier design

Compression heating of sauces

ITD value development

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 6: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Main projects

Capability development of innovative processes ( 112740)

HPTS optimisation algorithm (including ITD)

HP-T process logger

3 L Stansted HPP unit commissioning

Compression heating properties of water and waterglycol mixtures

Modelling Validation and Equipment (Re)Design of

Innovative ProcessesInnovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP

Cool Plasma characterisation

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 7: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 8: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline ndash Modelling HPTS

CFD modelling of HPTS in the Avure 35L vessel

Metal and PTFE carrier

Temperature mapping Validation of model

Inactivation modelling of C botulinum spores (based on thermal only linear

kinetics)

Enhancement of previous model

CFD modelling of improved 35 L HPTS model

Coupling CFD model to C botulinum inactivation models

Optimisation routine for PTFE carrier (temperature uniformity and heat retention)

CFD modelling of HPTS in Stansted 3L HPP unit

Various carrier designs

Inactivation modelling of C botulinum spores (various models)

Temperature mapping

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 9: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Physical principles of high pressure thermal sterilisation

Process conditions

Pressures up to 600-800 MPa

Moderate initial temperature 60-90ordmC

Holding times up to 5 minutes

Heat source

p

p

C

T

dP

dT

ρ

α=

t

PT

t

TC pp

part

part=

part

partαρ

Heat source

Location independent

Heat source

Compression heating up to sterilisation

temperatures

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 10: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Physical principles of high pressure thermal

sterilisation ndash ldquoTypicalrdquo pressuretemperature curve

Assuming no heat

losses during holding

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 11: 080815_postdoc_review_KK_shortened [Compatibility Mode]

High Pressure Thermal Processingndash Pressure and Temperature Distribution

Uniform pressure distribution

But temperature variation

through the vesselthrough the vessel

Characterisation of vessel re temperature distribution essential Measurement

Simulation

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 12: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Numerical modelling of high pressure sterilisation

Motivation

Process Assessment

Process and equipment modification

Develop new processing strategies while maintaining high standards

Equipment development and optimisation

eg scale-up studies

Industrial design solutions at reduced cost

eg scale-up studies

Industrial benefits

Reduced costs and time of experimentation and equipment use

Improved efficacy

Compared to analytical models

With

Utilisation of advantages and minimisation of disadvantages

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 13: 080815_postdoc_review_KK_shortened [Compatibility Mode]

CFD modelling of HPTS - Avure 35L vessel

Vessel + metal

carrier

Empty vessel

Vessel + PTFE

carrier

Vessel + carrier +

packages

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 14: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Modelling a 35 L HPTS vessel- The system and a computational model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

top water

entrance

z

r

carrier

water inlet

water

preheater

vessel

carriers

bullMetal

bullPTFE

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 15: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

Materials

Compression medium

Water

Carrier Structural steel

carrier

water

Carrier Structural steel

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 16: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Materials

Compression medium

Water

Carrier PTFE

Parameters and variables

Simulation of Flow and Temperature Distributions

using COMSOL Multiphysicstrade

carrier

water

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 17: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Distribution of spore reduction- Log-linear kinetics approach

Transformation of temperature distribution as function of

time into spore inactivation distribution

MATLAB routine

)(

log10)(N

NDdtyxF

ref

T

ref

T

t

z

TyxtT

sdot== intminus

3 scenarios

Empty vessel

Vessel including metal carrier

Vessel including PTFE carrier

0

0NrefTint

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 18: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Distribution of spore reduction

- linear kinetics approach

Effect of carrier presence

a) Empty vessel

b) Steel carrierb) Steel carrier

c) PTFE carrier

logS

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 19: 080815_postdoc_review_KK_shortened [Compatibility Mode]

logS-distribution

kill-distributionlogSlt-12

04

06

08

1

Kill

ratio [-]

holding

decompression

Distribution of spore reduction

- linear kinetics approach

250 300 350 400 4500

02

Time [s]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 20: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Mapping of Temperature Distributions using TC-Arrays and Image Processing - The system

35 L

HP

P v

essel

Carrie

r

Materials

Compression medium

Water

Carrier Structural steel

Parameters and variables

35 L

HP

P v

essel

Carrie

r

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 285 s

tdecompression = 15 s

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 21: 080815_postdoc_review_KK_shortened [Compatibility Mode]

35 L

HP

P v

essel

Carrie

r

Mapping of Temperature Distributions using TC-Arrays and Image Processing

35 L

HP

P v

essel

Carrie

r

Inside carrier

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 22: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Modeling a 35 L HPTS vessel- Validation of the simulated temperature distribution

Simulation

Only inside carrier (water)

1153 degC 1158 degC

Vessel

WaterCarrier

Measurement

2-D cross section

single time comparison

1165 degC 1161 degC

1086 degC1080 degC

3

6

12

45

789

TC array in an

axis-symmetric

cross-section

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 23: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Validation of the simulated temperature distribution

- 3x3 matrix 14 time steps

Good agreement was found between simulation and measured values

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 24: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Enhanced ModelSpore inactivation distributions and equipment optimisation

CFD ODE

T and flow distribution

Inactivation distribution

Computational model

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 25: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Modeling a 35 L HPTS vessel- Inclusion of vessel lid packages and carrier bottom valve

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

1

2

3

air layer

stainless steel

metal lid

top water entrance

z

r

7

6

5

4

3stainless steel

carrier

metal valve

water inlet

water

preheater

vessel

PTFE

carrier

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 26: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Modeling the 35 L HPTS vesselIncluding - vessel lid

- cylindrical packs- carrier bottom valve

Materials

Compression medium Water

Carrier PTFE

Vessel Structural steel

packs

vessel

air

COMSOL MultyphisicsTM

carrier

water

Vessel Structural steel

Packs Model food

Parameters and variables

Pressure 0-600 MPa

Tinit = 90 degC

tpressurize = 130 s

thold = 220 s

tdecompression = 15 s

air

End of holding time t = 350 s Initial conditions

t = 0

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 27: 080815_postdoc_review_KK_shortened [Compatibility Mode]

- Spore inactivation models

Log-linear kinetics

Weibull distribution

( ) ( )( ) ( )( )tTnttTbtN

sdotminus=log

))((

)(log

0 tTD

t

N

tNminus=(a)

(b)

Modeling inactivation distribution of C botulinum

nth order kinetics

Combined log-linear-nth order kinetics

( )( ) ( )( )tTnttTbN

sdotminus=0

log

( ) ( ) ( )( )[ ])1(1log1

1log

0

nttTtPknN

tNminussdotsdotminussdot

minus=

(b)

(c)

(d) If Tlt373 K (a) else (c)

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 28: 080815_postdoc_review_KK_shortened [Compatibility Mode]

-10

-8

-6

-4

-2

0

(a) Log-linear kinetics

(b) nth order kinetics

(c) Combined log-linear-nth

Modeling inactivation distribution of C botulinum- Distribution of spore reduction

-16

-14

-12

-10

(a) (b) (c) (d)

(c) Combined log-linear-n

order kinetics

(d) Weibull distribution

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 29: 080815_postdoc_review_KK_shortened [Compatibility Mode]

80

100

120

140

Tem

pera

ture

[ordmC

]

conventional retort process

high pressure process

Comparison HPTS and RetortF value 360 min can volume 384 mL

F value 356 min pack volume 346 mL

0 10 20 30 40 50 60 70 80

20

40

60

80

Time [min]

Tem

pera

ture

[ordmC

]

Retort process time

Preheating time

High pressure process time

Juliano et al 2007

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 30: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Comparison HPTS and Retort

-8

-6

-4

-2

0lo

g(N

N0)

A linear kinetics

B Weibull

C nth order

D linear-nth order

0 10 20 30 40 50 60 70 80-18

-16

-14

-12

-10

Time [min]

log

(NN

Retort process time

Preheating time

High pressure process time

HPTS

HPTS

HPTSHPTS

Retort

Retort

Retort

Retort

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 31: 080815_postdoc_review_KK_shortened [Compatibility Mode]

CFD models for optimisation - Motivation

Problem

Insulating carrier can occupy a large portion of the vessel

volume

Thickness of insulating material is often overdesigned

Solution

Reduce thickness while maintaining temperature uniformity Reduce thickness while maintaining temperature uniformity

and magnitude

Trial and error is hard to accomplish and too expensive

CFD approach allows for reduced costs and time of

experimentation and equipment use

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 32: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Finding the optimum- Parameters to consider

Required

Maximal usable volume ie wall thickness

Temperature uniformity

Temperature magnitudeduring holding time

Temperature magnitude

Measure for temperature performance

ITD value Evaluating temperature distribution and

heat retention

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 33: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Modelling high pressure thermal sterilisation- The system and the model

Flow Pressure Systems35L-600 sterilization machine (Avure Technologies USA)

Steel

wall

Variable carrier wall thickness

Carrier

HP chamber

preheater

vessel

carrier

Water

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 34: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Determination of optimum PTFE carrier thickness- CFD simulation and analysis

CFD runs through a number of wall thicknesses

0 mm le d le 70 mm

Carrier top and bottom size is fixed

Assumption PTFE shows no compression heating

Solve models and export temperature output

Carrier performance analysis

MATLABreg routine

Select temperature distr output at thickness d1

Define region of interest (inside carrier)

Calculate ITD at d1

Calculate usable carrier volume at d1

Repeat for other thickness values di

Plot ITD and usable volume vs wall thickness

Plot normalised values vs wall thickness

d = 0 mm d = 5 mm d = 70 mm

End of holding time t = 280 s

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 35: 080815_postdoc_review_KK_shortened [Compatibility Mode]

035

04

045

05

20

25

usable

volu

me L

06

07

08

09

1

norm

alis

ed v

olu

me a

nd IT

D

Determination of optimum- CFD simulation and analysis

Optimum

Intersection between ITD and usable volume curves

Optimum Optimum

PTFE heat sink effect PTFE heat sink effect

0 10 20 30 40 50 60 700

005

01

015

02

025

03

carrier wall thickness mm

ITD

-

ITD parameter

0 10 20 30 40 50 60 700

5

10

15

usable

volu

me L

usable vessel volume

0 10 20 30 40 50 60 700

01

02

03

04

05

06

carrier wall thickness mm

norm

alis

ed v

olu

me a

nd IT

D

normalised usable volume

normalised ITD

For perfect temperature performance 15 of maximum usable volume has to be sacrificed

Optimum of both ITD and usable volume dWall = 4 mm

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 36: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Summary ndash CFD models of Avure 35L HPTS unit

Model developed describing flow and temperature

distributions in a HPHT process

Validated for metal carrier

Coupled to log-linear thermal only inactivation kinetics (C botulinum)

Model improved

Vessel walls cool lid carrier valve packages

Platform for assessing models predicting C botulinum spore reduction

Optimisation algorithm developed

ITD value introduced assessing uniformity of treatment

Optimum carrier wall thickness found increasing usable volume by

more than 100

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 37: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Summary ndash CFD models of Avure 35L HPTS unit

Scientific impact

Paper in AIChE Journal (October 2007)

Paper in Biotechnology Progress (September 2008)

Paper on Optimisation in preparation (possible JFE SepOct 2008)

2 book chapters in ldquoEngineering Aspects of Thermal Processingrdquo

Article in FoodampDrink Magazine Article in FoodampDrink Magazine

3 posters at IFT 2007 (USA) ICEF 2008 (Chile)

4 oral presentations at GC Hahn Award Ceremony 2007 (Germany) ICEF

2008 (Chile) IFT 2008 (USA)

Commercial impact

HPTS2 project (key project area modelling HPTS in 3L vessel)

Potential new project modelling horizontal systems in 3D

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 38: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 39: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Determination of pressure and temperature

dependent compression heating factors

from adiabatic heating curves

1

15

x 10-10

P

a-1

Why determination of adiabatic heating coefficients necessary

Coefficients can be used to predict maximal achievable temperature upon pressurisation

of any material in HP process

Furthermore to predict the initial temperature from any maximum target temperature

Functions can be used as input source in CFD simulations of high pressure processes

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 40: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Protocols for determination of thermophysical properties

( )PTfp =α

dPC

TdTp

p

ρ

αsdot= For components

bull Liquids

bull Insulating polymers

bull hellipthermal expansion coefficient (K-1)

Ordinary differential equation (ODE) describing T-change upon P-change(adiabatic conditions)

)( PTfkC =

)( PTfC p =

( )PTf =ρ

MATLAB routine

4 Fit integrated ODE to pT-sub-

range

5 Extract compression heating

factors at specific T P

6 Fit values f = f(TP)

Experimental Part

1 Equilibrate to initial T

2 Apply pressure

3 Record pressure and temperature curve

Verification

7 Compare with water values

from NIST database

isobaric heat capacity (J kg-1 K-1)

density (kg m3)

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 41: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Experimental procedureEssentials

ldquoPerfectrdquo insulation required to

avoid heat loss or gain during

come-up time

High data acquisition rate

Thermocouple

Medium to be investigated

Plastic bottle

Centrifuge tube (plastic)

High data acquisition rate

Temperature

Pressure

Set to 200 ms log rate for

both P and T

investigated

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 42: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Experimental procedureOutput

Adiabatic heating curves for varying initial temperatures

100

110

120

130

0 100 200 300 400 500 600 700 800

20

30

40

50

60

70

80

90

Pressure MPa

Tem

pera

ture

ordm

C

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 43: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Determination of kC(PT)Fit to integrated ODE in all P intervals

dPkTdT Csdot= ( ) ( )0

0

PPkCeTPTminussdotsdot=

Integration

Assuming constant kC in each subP

Fit yields kC(subPsubT)

Repeat for all subintervals and a wide range of Tinit

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 44: 080815_postdoc_review_KK_shortened [Compatibility Mode]

1

15

x 10-10

P

a-11

15

x 10-10

k C P

a-1

Determination of kC(PT) example pure waterSurface fit (4th order) for PTkC

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k C P

a

280300

320

340

360

380

0200

400600

0

05

Temperature KPressure MPa

k

surface shapes are similar for both the ldquomeasuredrdquo kC values and the ones from NIST

for the range of Tinit = 5ordmC to 90ordmC investigated both curves give almost identical values

R2 = 09374

ldquomeasuredrdquo NIST database

R2 = 09961R2 = 09827

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 45: 080815_postdoc_review_KK_shortened [Compatibility Mode]

280300

320

340

360

380

0200

400600

0

05

1

15

x 10-10

Temperature KPressure MPa

k C P

a-1

Results at discrete concentrations - varying cGlycol yielding kC=f(PT)

280300

320340

360380

0200

400600

0

05

1

15

2

25

x 10-10

Pressure MPaTemperature Kk C

P

a-1

300

350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature K

Pressure MPa

k C P

a-1

c = 0400Pressure MPa

300

350

400

0

200

400

600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

300350

400

0200

400600

05

1

15

2

25

x 10-10

Temperature KPressure MPa

k C P

a-1

cGlycol = 0cGlycol = 25 cGlycol = 50

cGlycol = 75 cGlycol = 100

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 46: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Applying procedure to water and waterglycol mixtures

- Adiabatic heating as function of p and T0

dPkTdT Csdot=

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 47: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Summary

Adiabatic heating coefficients (kC) were determined as function

of P and T for

Pure water (proof of concept) ie cGlycol = 0

cGlycol = 25 50 75 100

Different concentrations show significant differences in Different concentrations show significant differences in

adiabatic heating

cGlycol is close to 36 in 3L unit and depending on fluid in carrier

changing from run to run

Ie there is a necessity to determine adiabatic heating of

processing fluid with kC = f(PTcGlycol)

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 48: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Supplemental for the determination of

compression heating of waterglycol

mixturesndash from discrete to arbitrary concentrations

kC(PTcG) =

250

300

350

400

0200

400600

800

-5

0

5

10

x 10-10

Temperature KPressure MPa

a

250

300

350

400

0200

400600

800

-1

-05

0

05

1

x 10-9

Temperature KPressure MPa

b

250

300

350

400

0200

400600

800

-4

-2

0

2

4

x 10-10

Temperature KPressure MPa

c

middot cG3 + middot cG

2 + middot cG +

+ kCNIST(PT)

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 49: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

Response surface equations (for all cGlycol) are used to calculate kC-

values at all pT combinations (0-700 MPa 5-125degC)

Yielding five 2D matrices (one for each cGlycol)

T

kC0

kC25

kC50

kC75

kC100P

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 50: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Approach for getting cGlycol-dependence - allocate kC-values to PT combinations

2D matrices are ldquosqueezedrdquo into one array

Yielding 2D array containing kC-vectors for all cGlycol

(0 M

Pa 125degC

)

kC50

kC75

kC100P

T

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0

cG100

cG75

cG50

cG25

cG0cG100

cG75

cG50

cG25

cG0

kC(0

MP

a 125

kC(7

00 M

Pa 125degC

)

kC(7

00 M

Pa 5degC

)

kC(0

MP

a 5degC

)

kC0

kC25

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 51: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Approach for getting cGlycol-dependence - perform fit at each PT combination

3rd order polynomial is fitted to kC-vector at each PT

combination

kC(cGlycol) = amiddotcGlycol3 + bmiddotcGlycol

2 + cmiddotcGlycol + kCNIST

With kCNIST being the the adiabatic heating With kCNIST being the the adiabatic heating

coefficient of pure water ie cGlycol = 0

Yielding values for abc as well as R2 at each PT

combination

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 52: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Approach for getting cGlycol-dependence - step4 perform surface fit for 3rd order polynomial coefficients

300350

400

0

500-4

-2

0

2

4

6

x 10-10

a

250

300

350

0

200

400

600

-1

-05

0

05

1

x 10-9

b

250

300

350

0

200

400

600

-4

-2

0

2

4

x 10-10

c

250300

1000 Pressure MPa

Temperature K

350

400

600

800Temperature K

Pressure MPa

350

400

600

800 Temperature KPressure MPa

Surface fits (4th order) of the previously determined 3rd order polynomial fit coefficients yields abc = f(PT)

ie kC(PTcGlycol) = a(PT)middotcGlycol3+b(PT)middotcGlycol

2+c(PT)middotcGlycol+kCNIST(PT)

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 53: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Validation of approach - comparison of predicted and measured T curves

360

380

400

420

Tem

pera

ture

K

360

380

400

Tem

pera

ture

pre

dic

ted K

R2 = 099948

Predicted (from cGlycol-dependent kC) and measured pT-curves show a good agreement yielding an R2 of 099948

cGlycol = 30

0 1 2 3 4 5 6 7

x 108

260

280

300

320

340

360

Pressure Pa

Tem

pera

ture

K

280 300 320 340 360 380 400

280

300

320

340

360

Temperature measured K

Tem

pera

ture

pre

dic

ted K

bisecting line

Tinit

= 4ordmC

Tinit

= 43ordmC

Tinit

= 91ordmC

from c-dependent kC

PT measurements

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 54: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Summary ndash Compression heating properties

Methodology for measuring compression heating properties of

liquids and semi-solids developed

Validated for deionised water

WaterGlycol mixtures 0 25 50 75 100

From discrete to arbitrary concentrations

Next steps

Paper well in progress

Modify methodology for insulating carrier materials

Measure compression heating properties of solids

And a range of food products andor model food substances

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 55: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 56: 080815_postdoc_review_KK_shortened [Compatibility Mode]

HP-T process logger

- A novel approach for measuring - A novel approach for measuring

temperatures at HPHT conditions

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 57: 080815_postdoc_review_KK_shortened [Compatibility Mode]

In HPHT processing accurate temperature control is

indispensable

Heat retention aids (eg insulated carriers) are not always reliable

Thermocouple Issue

Fail after several runs

Readings may be disturbed by internal heaters

Motivation

Readings may be disturbed by internal heaters

Wireless systems needed

Temperature mapping of empty carriersvessels hellip

hellip also filled carriers

Tracing of process temperature

Assistance in regulatory approval

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 58: 080815_postdoc_review_KK_shortened [Compatibility Mode]

The shell

Highly stress resistant

Low specific heat capacity

heat sink effect minimal

High thermal conductivity

The data logger

Wireless temperature logger

The system = pressure resistant shell + data logger

Prototype

stable for more than 70 runs

P = 600- 800 MPa

and

T le 130degC

Temperature range 0ordmC le T le 130ordmC

Measurement intervals ge 1 s

Memory 4000 logs per run New design

No clamps

required

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 59: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Delayed readings due to

Temperature loggerrsquos

inherent delay

Heat transfer

through

The system = pressure resistant shell + data logger- The problem

55

60

65

thermocouple

thermochron

500

600

pressure

HP-T logger

through

aluminium shell

Multi-step HP process

0 500 1000 1500 200035

40

45

50

55

time s

tem

pera

ture

ordm

C

0 500 1000 1500 20000

100

200

300

400

pre

ssure

M

Pa

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 60: 080815_postdoc_review_KK_shortened [Compatibility Mode]

The solution ndash Multistep algorithm

Reverse logic algorithm

Step 1 calculates temperatures inside the

shell accounting for delayed readings of

temperature logger

Step 2 performs ldquoself-calibrationrdquo of device Step 2 performs ldquoself-calibrationrdquo of device

Step 3 predicts temperature outside the shell

based on energy balance

dt

dTmcQ P=amp ThAQ ∆=amp

dt

dT

hA

mctTtT

shellinPshellinreal

_

_ )()( +=

Energy required to heat the shell Energy flow due to temperature difference

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 61: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Validation of measurements- Retort trials T = 121ordmC p = 2 bar

105

110

115

120

125

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

Magnified view of end of holding stage

Magnified view of end of temperaturecome-up stage

400 500 600 700 800 900 1000 1100

100

time s

recalculated temperature outside

thermocouple in retort

2600 2700 2800 2900 3000 3100 3200

100

105

110

115

120

time s

tem

pe

ratu

re ordm

C

TC measurement

recalculated temperature in shell

recalculated temperature outside

thermocouple in retort

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 62: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Validation of algorithm- with thermocouple in HP trials 3L system

45

50

55

60

Tem

pera

ture

ordm

C

thermocouple data

HP-T logger data

60

65

R2 = 09906

mCphA = 69

Parity plot shows very good agreement

Initial T = 45ordmC

P = 01-300-450-600-400-150 MPa

0 500 1000 1500 200035

40

Time s

35 40 45 50 55 60 65

35

40

45

50

55

Tthermocouple

ordmC

TH

P-T

lo

gg

er

ordmC

p

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 63: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Summary ndash HP-T process logger

Aluminium shell

Prototypes have proven to be stable at high pressure and high temperature

conditions

Due to low thermal mass heat sink effect minimal

Latest design does not require clamps easier to handle

Software

Reverse logic algorithm accounts for both the delay caused by the logger

and the shell

Instantaneous readings without delay

Self-calibration possible

Business Development

Industry highly interested in logger

Presentation and Brochure at IFT 2008

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 64: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Socket for chip connected to battery

Chip

Future

microHP-T Process Logger

- A potential miniature version of the HP-T process logger

+

-

Chip

Battery

Plug for USB reader

Heat shrink

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 65: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 66: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Further project involvement Ultrasound projects

Starch modification by high and low frequency ultrasound (co-supervision of PhD student)

Ultrasound assisted tomato break (commercial)

Wool wax recovery (CSIRO TFT)

Chips modification (commercial)

Calcium infusion in pears (commercial)

Food Futures Flagship

NMR methodology for diffusion coefficient measurement NMR methodology for diffusion coefficient measurement

Rheology model fitting

Temperature mapping of drying oven

High Pressure Processing

Several internal projects (HP-T logger Tinit-determination to reach Ttarget hellip)

Operating HP unit (commercial)

HPTS concept product development (co-supervision of work experience student)

PEF modelling (thermal-only)

And more hellip

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 67: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Outline

Contribution to FSArsquos Key Success Indicators

Main projects

Core research areas

Modelling High Pressure Thermal Sterilisation (HPTS)

Compression heating properties Compression heating properties

Development of temperature loggers for HPTS processes

Other projects

Future Work

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 68: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Future Work

Modelling Validation and Equipment (Re)Design of Innovative Processes

HPTS modelling (Validation)

Compression heating properties (insulating materials)

Inactivation model development

PEF modelling

US modelling

Cost modelling of HPP Cost modelling of HPP

Cool Plasma characterisation

Publications and Conferences

2-3 papers on compression heating properties as function of P and T

2 papers as outcome of HPTS2 project

Papers as outcome of students projects (PEF and Cool Plasma)

Book on ldquoMultiphysics Modelling of Emerging Food Processing Technologiesrdquo

Symposium at IFT 2009 on ldquoAdvanced Modelling of Innovative Processesrdquo

Thank you

Backup slides

Page 69: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Thank you

Backup slides

Page 70: 080815_postdoc_review_KK_shortened [Compatibility Mode]

Backup slides