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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
- 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
-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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Thank you
Backup slides
Backup slides