A generic modelling and simulation platform for assessing ... · Fermentation Beer processing Boiler Water preparation Utilities Waste management Malting Brewhouse Fermentation Beer_processing

A generic modelling and

simulation platform for

assessing novel malting

and brewing technologies

Mr. Eemeli Hytönen (PhD), Ms. Lotta

Sorsamäki and Ms. Marja Nappa

VTT Technical Research Centre of Finland, Ltd.

EBC Symposium, Wrocław, 18-20 September 2016

PBL Brewing

Laboratory

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Content

Background

Objective

The platform

Examples

Conclusions

Acknowledgements

Hytönen E., et al., 20.9.2016, EBC Symposium



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Background

The work presented here has been developed together with PBL

Brewing Laboratory and partially within an ongoing Eco-efficient

malting and brewing processes -project

The overall goal of the project is to create knowledge and

prerequisites that, compared to the present technology, enable

the development of ecologically more efficient processes for

malting and brewing

Specifically research focus has been on purification and reuse of

malting process waters and opportunities for saving energy in

cooling and drying




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Background

Key indicators and significant cost factors for the industry are water and energy use, e.g.

116,8MJ/hl energy was needed on average in European breweries (2010). The variation is very

large, between 70,6 and 234,1MJ/hl, resulting from varying brewing landscape across Europe a)

Energy use has been reported to equal 3…8,5% of beer production costs but varies very much

depending on for example the beer type or technological age of the brewery b)

The true cost of water is more than sum of the water price and sewer service costs c)

Specific water consumption on average in European breweries in 2010 was 4,2hl/hl beer, of which

2,7hl/hl beer was discharged as wastewater a)

Technological solutions for more sustainable brewing industry are constantly being

developed in R&D projects. These solutions target also energy and water efficiency

improvements

A systematic approach at conceptual level was seen needed to quantify the key indicators

for new developments and technological solutions. Between 2012-2016 a tool/platform was

developed with emphasis first on brewery and later on malting process


a) C. Donoghue et al., The Environmental Performance of the European Brewing Sector, Report number 3101010DR02, May 2012

b) Galitsky et.al., Energy Efficiency Improvement and Cost Saving Opportunities for Breweries - An ENERGY STAR® Guide for Energy and Plant

Managers, LBNL-50934, September 2003, based on data from Sorrell, 2000, McDonald, 1996, Anheuser-Busch, 2001

c) Chastain et al., Brewers Association Water and Wastewater: Treatment/Volume Reduction Manual, Brewers Association



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BackgroundExamples of simulation tools used in brewery/malthouse design/analyses


Tool Tool’s provider, focus, wwwPurpose of

simulation

Scope (plant

wide,

department,

components)

Type (code,

commercial

simulator,

spreadsheet,…)

Example references of use

SuperPro

Designerhttp://www.intelligen.com/

M&E,

schedulingplant wide

Commercial

simulator

Jones A., et al., Team iBrew design report, Calvin

College, 2013

iSILOGhttp://www.isilog.de/en/produkte/loes

ungen/brauerei-loesung.html

M&E,

dynamicsplant wide

Commercial

simulator

http://www.isilog.de/images/pdfs/Siemens-PLM-

Paulaner-cs-Z11.pdf

Batches http://www.bptechs.com/Energy,

dynamicsdepartment

Commercial

simulator

Mignon D. and Hermia J., Using batches for modeling

and optimizing the brewhouses of an industrial

brewery, Computers & Chemical Engineering, 1993,

Vol 17 (supplement 1), S51-S56

MatLab –

simulink

http://se.mathworks.com/products/si

mulink/?requestedDomain=www.mat

hworks.com

process

controldeparment code

Warnasooriya, Modeling and simulation of the beer

fermentation process and temperature control, 2011,

Master's Thesis

MatLab –

simulink

http://se.mathworks.com/products/si

mulink/?requestedDomain=www.mat

hworks.com

M&E plant wide codeBleier B., et al. Craft Beer Production, Design report,

Unviersity of Pennsylvania, 2013

Excel Energy plant wide

Spreadsheet

using

Engineering

Equation solver

(EES)

Muster-Slawitsch B. et al., Process modelling and

technology evaluation in brewing, Chemical

Engineering and Processing 84 (2014) 98–108

Excel Dynamics components

Spreadsheet for

dynamic

component

balances

Krogerus K., Gibson B. and Hytönen E., "An improved

model for prediction of wort fermentation progress and

total diacetyl profile", the Journal of the American

Society of Brewing Chemists, 2015 (1): 90-99

Aspen

Plushttps://www.aspentech.com/

M&E, steady-

stateplant wide

Commercial

simulator

Fei Yu, Process modeling of very-high-gravity

fermentation system under redox potential-controlled

conditions, Master's Thesis, University of

Saskatchewan, 2011

http://www.intelligen.com/http://www.isilog.de/en/produkte/loesungen/brauerei-loesung.htmlhttp://www.isilog.de/images/pdfs/Siemens-PLM-Paulaner-cs-Z11.pdfhttp://www.bptechs.com/http://se.mathworks.com/products/simulink/?requestedDomain=www.mathworks.comhttp://se.mathworks.com/products/simulink/?requestedDomain=www.mathworks.comhttps://www.aspentech.com/



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Objective

Investigate impacts of technological choices and implementation

of novel technologies on malting and brewing processes

Impacts of interest: energy and water consumption

Develop a holistic and flexible platform for R&D projects’ impact

analysis that is based on plantwide modelling of malting,

brewing and linked processes




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The platform

Superstructure-type steady state simulation model

The key performance measures evaluated using the platform are plant wide

and departmental energy and water consumption and equipment utilisation

degree.

Platform uses two interlinked software

Process simulation model for mass and energy balance using Balas® process

simulator *

Microsoft Excel -based spreadsheet system for electricity consumption and unit

operation utilisation degree calculations

User interface in Excel for parameterization and result manipulations

Additional automation build to handle systematically data: the setting-up a model

run, conversion of M&E balances (process demands) to water and energy

consumptions and unit utilisation, storing results

* balas.vtt.fi


http://balas.vtt.fi/



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The platformFlexibility


Unit operation level flexibility

Brewhouse

mash filtering: lauter tun or filter

mash milling: wet or dry

weak wort recycling optional

Trub recycling optional

Beer processing

pasteurization optional

Malting:

Steeping: amount of steeps,

water recycling rate, optional

water purification

Optional barley drying

Superstructure-type

process model + linked

configuration and

management = Flexibility

Platform level flexibility:

heat source: hot water or

steam

cooling: EtOH/water,

ammonia

Case comparisons

setting-up scenarios



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The platformProcess simulation model

Thermodynamic properties

VLE calculated using thermodynamic model RKS – Redlich-

Kwong-Soave

Model component data mainly from Reid et al. *

Liquid phase assumed to be ideal

Model compounds:

Water, Ethanol, Carbon dioxide, Oxygen, Nitrogen and

Ammonia

Malt and adjuncts (brewhouse): Water and solid Starch

Malt (malting): Starch, Protein, Beta-glucan, Barley-other and

Water

Syrup: a binary mixture of Water and liquid Glucose.

Hops and yeast: a binary mixture of Water and solid Hops and

solid Yeast (thermodynamic properties the same as for

cellulose)

Cans: Aluminium

Trub: Lipofilics

Reactions:

Yield –based (kinetics not considered in the reactors)

Reaction heat either based on literature or actual reaction heat

based on the thermodynamic properties


* Reid, Prausnitz, and Sherwood: The Properties of Gases and Liquids - Third Edition, McGraw-Hill, 1977.

Departments

Malting

Brewhouse

Fermentation

Beer processing

Boiler

Water preparation

Utilities

Waste management

BrewhouseMalting Fermentation Beer_processing

Boiler Water_prep Waste_mgt

Feedstock

0.745 kg/s 15 C 101 kPa

Clean_water

33.9 kg/s 10 C 101 kPa

Utilities

Beer

5.56 kg/s 10 C 200 kPa

Hops

0.006 kg/s 15 C 101 kPa

Feed

0.241 kg/s 84.2 C 101 kPa

Waste_water

25.2 kg/s 19.5 C 101 kPa

Oxygen

Warm_water

2.68 kg/s 65.6 C 101 kPa

Syrap

Feedstock_in

Hops_in

Syrap_in

Oxygen_in

Adjuncts_in

Adjuncts

CO2_in

CO2

Yeast_in

Yeast

Waste_yeast

0.036 kg/s 9.13 C 150 kPa

Recovered_CO2

0.19 kg/s -26.6 C 1600 kPa

Rootlets_waste

MAIN FLOWSHEET

LP_steam_in

LP_steam

0.517 kg/s 0.517 MW

LP_condensate

0.517 kg/s 142 C 500 kPa

Cold_water_in



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The platformProcess simulation model – screenshot of brewhouse flowsheet


Miller

Malt_in

Milling

Mash filtering

Wort boiling

Wort filtering and cooling

Mashing

Liquor_in_mashing

50.8 C

Mashing_liquor

55 C

Mash_liquor_valve

Mashing_heater_2 Mashing_heater_3Mashing_heater_1

79

.4 C

1.5

9 k

g/s

Spent_grain

Mashing_vessel_1 Mashing_vessel_2 Mashing_vessel_3 Mashing_vessel_4 FC

_M

ash_

filte

r_sp

arg

ing

Sweet_wort

76.5 C

155 C 0.289 kg/s

70 C 75 C

Wort_boiler

Wort_to_filtering

Hops_to_boiling

Hops_in

87.5 C

FC_hops

Hops_trub

Hop_trub

Wort_to_cooling

99.7 C

85 C

Wort_to_fermentation

3.41 kg/s 10 C 101 kPa

Wort_out

Brewhouse_s_conds_out

LP_BrewhouseMashing_liquor_tank

4 C

Brewhouse_warm_Water_out

2.68 kg/s 65.6 C 101 kPa

Wort_boiling_condenser

0.186 kg/s 25 C 101 kPa

Wort_boiling_cond

79

.4 C

6.6

3 k

g/s

94

.5 C

6.6

3 k

g/s

Chilled_w_wort_cooling

Warm_wtr_tank

SpentGrain

Syrap_in

Syrap_to_boiling

Adjucts_in FC_Adjuncts

FC_SyrapWort_boiler_sp

87.5 C

99 C

Energy_tank

85

C 3

.95

kg

/s

94

.5 C

3.9

5 k

g/s

10 C 0.621 kg/s

62 C

Wort_cond_cooler

10

C 0

.18

4 k

g/s

Additions_sp

90 C 0 kg/s

We

ak_

wo

rt

Split=0; no trub recycled

Split=1; trub recycled

Split=0; trub to mashing

Split=1; trub to filtering

Trub_formation

Split=0; weak wort to filtering

Split=1; weak wort to mashing

Pre_wort_separator

Mash_filter_press

Pre_wort

Mash_drain_wtr

Main_wort

Split=1; mash filter

Split=0; lauter tunPre_masher

BREWHOUSE

Mashing_sp

10

C 0

kg/s

GA-206

GA-202

GA-201

GA-102

GA-606

GA-605

GA-104

GA-604

GA-204

GA-203

GA-101

GA-207

GA-205

GA-103

Mashing_loss

Boiling_loss

Prerun_vessel



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The platformProcess simulation model

Approach for making a steady-state process model from batch processes

#1 – If constant conditions (T, p, moisture) average flow through a batch unit in unit of time equals the flow rate

in corresponding continuous model unit

#2 – If conditions change (e.g. heat profile, gas venting) the batch unit is divided into representative ”phases”

for which #1 can be assumed to apply. In the model, consecutive phases are modelled using a series of units

#3 – All batch equipment have specific volume and number of vessels defined for utilisation degree evaluation

Examples

Mashing Fermentation

1 batch unit 4 phases 1 batch unit 2 phases




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The platformLinked spreadsheet model

Electricity (Brewing)

consumption breakdown

Consumption in pumps (~30

pumps dimensioned) & process

cooling is calculated using M&E

balances

Electricity (Malting)

Equipment utilisation

Effect of process changes on

needed equipment volume per

time unit

maximum theoretical

utilisation degree used as

baseline

Both continuous (e.g. mash

filtering, wort filtering, beer

filtration) and batch (mashing,

boiling, fermentation)

equipment assessed


Summer Winter

Kilning and Germination, including

possible cooling

80 % 69 %

Product and barley handling, steeping 15 % 24 %

Other (laboratory, office) 5 % 7 %

Share of total consumption

Machine drive and process cooling 55%

Other equipment 25%

Process HVAC and lighting 15%

Other 5%



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ExamplesCase study definition


CASE Basecase VHG High moisture

malt

Beer production (ML/a) 150 220 a) 150

Malting capacity (kt dry/a) 20 20 22.2 a)

Gravity after wort boiling (Plato

number)

15 22 15

Malt moisture (%) 4.8 4.8 12

Syrup dose (g/kg malt) 0,01 100 0,01

Fermentation

• Temperature (°C)

• Duration (h)

• Cycle duration (h)

• O2 to aeration (mgO2/kg wort)

10

144

290

10

17

168

338

15

10

144

290

10

Milling specific energy (kWh/t malt) 5.6 5.6 8.1 b)

Wort boiling time (min) 60 60 74 b)

Brewhouse yield (%) 75 75 70 b)

a) Simulation result; b) Experimental result, note: atmospheric wort boiling

Objectives of the case study:

assessment of the impacts

of very high gravity brewing

on a brewing process

balances

evaluation of the impacts of

malt moisture on a malt

house and brewery integrate

balances

Basecase and two other

cases used; main parameters

in the table

VHG – very high gravity;

design capacity basis is

constant wort boiling

capacity

high moisture malt case

design capacity to fulfill

basecase beer production

rate



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ExamplesBasecase – M&E balance and example of platform validation

Inputs to and outputs from

the brewery

All inputs and outputs back-

calculated based on the

setpoint of 150ML/a beer

with gravity 15 after wort

boiling

Energy consumption values

only for brewery

Validation of the simulated

electricity demand using

literature:

Simulated value (7.2kWh/hl

beer) a bit lower than

published values

(>7.5kWh/hl beer in

Europe) *


*Scheller, L., Michel, R. and Funk, U. Efficient Use of Energy in the Brewhouse, MBAA TQ vol.45, no.3, 2008 , pp. 263-267



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ExamplesVHG-case compared to Basecase

Energy consumption

values only for

brewery

When gravity is

increased from 15 to

22, 47% increase in

beer production, 36%

increase in malt or

grain demand and

significantly increased

by-product production




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ExamplesVHG-case compared to Basecase

With the case study

assumptions, moving to VHG

brewing can significantly

decrease energy demand and

somewhat water demand

When gravity is increased to

22 considering same wort

boiling capacity, processing

after fermentation requires

more capacity upto 47% in

high gravity beer (HGB)

adjustment and pasteurization


Basecase VHG

MASHING 100 % 65 %

MASH FILTERING 100 % 65 %

WORT BOILING 100 % 100 %

WORT FILTERING 100 % 100 %

FERMENTATION 100 % 117 %

BEER FILTRATION 100 % 97 %

HGB ADJUSTMENT 100 % 147 %

PASTEURIZATION 100 % 147 %

Table. Brewery utilisation degree



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ExamplesHigh moisture malt case compared to basecase


When malt moisture

is increased from

4.8% to 12%, only

small impacts on

overall balances is

expected based on

the assumptions

made in this study

Energy consumption

values include both

malting and brewing

Due to lower yield

however, more grains

are needed to

produce the same

amount of beer as in

basecase



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ExamplesHigh moisture malt case compared to

basecase

Increasing the malt moisture seems to

lower the energy needs of malting but

due to assumed yield loss in mashing

the total energy consumption is about

the same as in basecase

In order to be able to accommodate

higher moisture malt in brewery, for

same production rate more capacity is

needed mainly in fermentation


Basecase High moisture malt

MASHING 100 % 103 %

MASH FILTERING 100 % 102 %

WORT BOILING 100 % 101 %

WORT FILTERING 100 % 100 %

FERMENTATION 100 % 100 %

BEER FILTRATION 100 % 100 %

HGB ADJUSTMENT 100 % 100 %

PASTEURIZATION 100 % 100 %

Basecase High moisture malt

STEEPING 100 % 111 %

GERMINATION 100 % 111 %

GERMINATION AIR 100 % 100 %

KILNING 100 % 72 %

KILNING AIR 100 % 72 %

Table. Brewery utilisation degree

Table. Malt house key process utilisation degree



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ExamplesHigh moisture malt -case result validation (malting process)

Malting-process heat consumption: model compared to values derived

from process data in different conditions

Energy consumption values for malting vs. literature


Heat (kWh/t malt) Electricity (kWh/t malt)

Literature 614 – 1066 a), 713-1105 b) 77.4 – 156 a),113 – 171 b)

Platfrom (basecase) 700 100

a) Electricity consumption matches actual demand at Danish Malting Group, Danish energy agency; b) Stewart, D., Emissions, energy, water and

malt, Brewer & Distiller International, May 2010. 38-41.



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Conclusions

A generic modelling and simulation platform has been developed for

investigating impacts of technological choices and implementation of novel

technologies on malting and brewing processes

The main features of the linked modelling platform and specifically the

simulation model have been presented.

The example case studies presented were:

assessment of the impacts of very high gravity brewing on a brewing process

evaluation of the impacts of malt moisture on a malt house and brewery integrate

Case study results show positive impacts on both energy and water demands

in the VHG case

The platform has shown its targeted features:

flexible – detailed malting department model added and linked to overall simulation

model with less model compounds; easy set-up and comparison of new cases

holistic – plant-wide somewhat non-intuitive balances quantified for high moisture

malt case show even slightly higher energy demand




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Acknowledgements

The authors would like to acknowledge

PBL Brewing Laboratory, Ecomalt project and Tekes for funding

the modelling and platform development work

All project and company experts involved for their valuable inputs

to the contents and structure of the platform




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Contact

Mr. Eemeli Hytönen, PhD

Principal Scientist

VTT

P.O.Box 1000

FIN 02044 VTT, Finland

Tel. +35820 722 2729

Mobile +35840 533 6759

E-mail: [email protected]


mailto:[email protected]

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A generic modelling and simulation platform for assessing ... · Fermentation Beer processing Boiler Water preparation Utilities Waste management Malting Brewhouse Fermentation Beer_processing