1

Želimir Kurtanjek

Computer aided modelling, design and development of processes in food industry

Faculty of Food Technology and BiotechnologyUniversity of Zagreb

Pierottijeva 6, 10000 Zagreb, CROATIAzkurt@pbf.hr

2SCOPE

1. Objectives for computer aided process design and development

2. Information systems in industry

3. Computer aided modelling techniques and software

4. Examples

3. Conclusions

3

1. market demand

3. material resources

4. financial resources

8. product diversification

Limitations for investments in food production

6. technology

5. environment impact

7. product quality

2. human resources (knowledge)

Most of the limitations can be analysed and solutions optimised by use of computer models

4

Effects of modelling and optimisation in industrial production

MANAGMENT

PROCESS DESIGN

SHORT PRODUCTION PLANS

financial effects time span

1-5 years

0,1-1 year

1-7 days

5

FEASIBILITY STAGE

Evaluation of product opportunities

DEVELOPMENT STAGE

Development objectives and budget planning

MARKET ENTRY

Decision steps in production investments

New product candidates

Product delivered to market

Level 1

Level 2

Level 3

Level 4

Level 5

6Level Type of (model) estimates for an

investment for 100 mil $ plant

Accuracy Cost (1000 $)

1 Order of magnitude estimation

focus: innovations, creativity,

< 50 % --- ? -------

2 Project planing estimates

focus: alternatives in process synthesis

< 30 % 20 - 40

3 Preliminary engineering

focus: budget estimate

< 25 % 50 - 100

4 Detailed engineering

focus: executive planning

< 15 % 100 - 200

5Procurement and construction:

focus: contracting

< 10 % 1000 - 2000

7CHARACTERISTICS OF

MODEL DEVELOPMENT

1. APPLICATION OF SYSTEMS VIEW

2. INTEGRATION OF KNOWLEDGE :

thermodynamics, transport phenomena, biochemistry,

mathematics, computer science

3. APPLICATION OF AI ALGORITHMS FOR AUTOMATED

INFORMATION BASED MODELING

fuzzy logic modelling, neural networks

8 Systems theory view on industrial production

SUSTAV

OKOLINAGRANICASUSTAVA

{masa

energija

informacija}

masa

energija

informacija

9

Surroundings

System

xP

xI

y

Process subsystemSP

Control subsystemSC

Systems view integrates production plant and information (process control + management)

10Graphical representation of "transparency" of mathematical

models in relation to knowledge and perception of complexity of a system.

Neural networks

Fuzzy models

Analytical models

System complexity

Knowledge

X Y

11

Schematic diagram of mathematical forward M and inverse M-1 models as mapping between input X and output Y sets

X Y

M

M-1

12Analytical models are derived by mass, energy

and momentum balances

V

U

U

U

I

I

1

2

3

1

2

V

U

U

U

I

I

1

2

3

1

2

u

u

i

i

1

2

1

2

S

U

U

U

1

2

3

ukupanvolumen V

diferencijalvolumena

dV

I

I

1

2

u

u1

2

i

i12

U,I su ulazni i izlazni tokovi za ukupanvolumen

u , i su ulazni i izlazni tokoviza diferencijal volumena

S

13

fttx

yty

txtyftydt

d

,0

0

,

0

General structure of a process dynamic model in a finite dimensions state space

14

STRUCTURE OF FUZZY LOGIC MODELS

Xtemperature

pH…etc.….

productivity

product quality

…etc.….

Y

Input space is “described” by “linguistic” variables:

Temperature

initial, low, optimal, high, very high

pH

initial, low, optimal, high, too high

Output space is “described” by “linguistic” variables:

Productivity

low, average, optimal

Quality

low, average, standard, high

Fuzzy logic

INFERENCE

rules

15

Fuzzy sets are associated with linguistic variables

Fuzzy sets are defined by membership functions

1 2 3 4

0.2

0.4

0.6

0.8

1

Tmin Tmaxtemperature

μ(T)

μ4μ3

μ2

μ1μ5

initial

low

optimalhigh

very high

16

pHmin pHmax

μ( pH)

μ 1 μ 2 μ 3 μ 4 μ 5

pH

Membership functions for pH

17

FIS model: Product quality=f(T,pH)

FUZZYRULES

INPUTSPACE OFLINGUISTICVARIABLES

FUZZIFICATION

OUTPUTSPACE OFLINGUISTICVARIABLES

DEFUZZIFI-CATION

FUZZY INFERENCE SYSTEM

INPUT DATA T(t) pH(t)

OUTPUT DATA quality(t)

AGGREGATION

18

Neural network models

Artificial neural network models are mathematical (computer models) of biological nervous systems

Neural

network X Y

19Schematic representation of a neurone with a sigmoid activation

function

O

x1

x3

x2

xi

xN

ACTIVATION

0

0,2

0,4

0,6

0,8

1

1,2

-6 -4 -2 0 2 4 6

INPUTO

UTP

UT

)exp(1

1)(

ssf

20Schematic diagram of a feedforward multilayer

perceptron

Y3

Y2

Y1

X1

X2

X3

X4

I H O

21

Applications

Fuzzy logic models

Modelling human knowledge (reasoning)

Process regulation

Product recipes

Product quality assurance

Neural networks

Modelling of complex systems

Image analysis

Electronic noses

Process automation, Robotisation

Control of fermentation

22

COMPUTER AIDED PROCESS SYNTHESIS

INPUT OTPUT MODELS

OF UNIT OPERATIONS

+

PROPERTY DATA

+

NUMERICAL METHODS FOR

BALANCE SOLVING

RIGOROUS: THERMODYNAMIC MODELS OF REAL SYTEMS

PRELIMINARY: THERMODYNAMICS OF IDEAL SYSTEMS

23SOFTWARE FOR COMPUTER AIDED PROCESS

DESIGN

ASPEN PLUS

Rigorous design

INTELLIGEN, INC.

BIO DESIGNER

ENVIRONMENT DESIGNER

SUPER DESIGNER

Feasibility design

24

Aspen Plus makes it easy to build and run a process simulation model byproviding a comprehensive system of online prompts, hypertext help,and expert system guidance at every step. In many cases, user is able to develop an Aspen Plus process simulation model without referring to printed manuals.

ASPEN PLUS

Process simulation to predict the behavior of a process by using basic engineering relationships, such as mass and energy balances, and phase and chemical equilibrium. Given reliable thermodynamic data, realistic operating conditions, and rigorous equipment models can simulate actual plant behavior. Process simulation enables to run many cases, conduct "what if"analyses, and perform sensitivity studies and optimization runs. With simulation, design of better plants and increase profitability in existing plants is possible.Process simulation is useful throughout the entire lifecycle of a process, fromresearch and development through process design to production.

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INLET STREAM: ID1 OUTLET VAPOR STREAM: ID2 OUTLET LIQUID STREAM: ID3 PROPERTY OPTION SET: NRTL RENON (NRTL) / IDEAL GAS

*** MASS AND ENERGY BALANCE *** IN OUT RELATIVE DIFF. TOTAL BALANCE MOLE(KMOL/HR ) 50.0000 50.0000 0.000000E+00 MASS(KG/HR ) 1251.44 1251.44 0.181690E-15 ENTHALPY(CAL/SEC ) -853878. -853880. 0.182470E-05

30SuperPro Designer®

A Computing Environment for Designing and Optimizing Integrated Specialty Chemicals, Biochemical, Pharmaceutical, Food, Packaging,Water Purification, Wastewater Treatment and Air Pollution Control Processes

INTELLIGEN, INC. - 2326 Morse Avenue - Scotch Plains,

NJ 07076 - USA

31Integrated Cheese Plant

This example deals with an integrated milk processing plant that produces cheese, butter, whey protein concentrate (WPC), and food-grade ethanol.

The plant operates around the clock for 330 days a year and on a daily basis processes 2,000 metric tons of milk (83,333 kg/h) and produces 214 tons of cheese, 9 tons of butter, 211 tons of WPC, and 28 tons of ethanol.

The plant consists of four sections: Cheese Making, Butter Making, WPC Making, and Ethanol Making.

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Raw Material kg/Year kg/Hour kg/MP Entity

Milk 660,000,000 83,333.3 168.11 Starter 6,600,000 833.3 1.68 Renin 660,000 83.3 0.17 Salt 1,412,811 178.4 0.36 Yeast 3,009,600 380.0 0.77

Total 671,682,411 84,808.4 171.09

MP Entity = Main Product Entity = 18 kg cheese block

36

COST ANALYSIS AND ECONOMIC EVALUATION

Cost Item $/Entity $/Day $/Year %

Raw Materials 36.15 430,114 141,937,515 76.98 Equipment 5.72 68,101 22,473,334 12.19 Labor 1.89 22,473 7,416,000 4.02 Consumables 0.63 7,451 2,458,912 1.33 Lab/QC/QA 0.09 1,124 370,800 0.20 Waste Trtm/Disp 0.24 2,881 950,763 0.52 Utilities 2.24 26,610 8,781,366 4.76

Total 46.97 558,754 184,388,691 100.00

37

Information on Capital Investment and Project Economic Evaluation follows below:

Equipment Purchase Cost ($) 18,417,000Direct Fixed Capital ($) 119,782,000Working Capital ($) 14,462,000Total Capital Investment ($) 140,233,000

Annual Operating Cost ($) 184,389,000

Revenues ($/year)Cheese 176,671,000WPC 6,201,000Ethanol 15,052,000Butter 7,901,000

Total Revenues 205,825,000

Return on Investment (%) 17.3Payback Time (years) 5.78IRR (after taxes) (%) 11.2

38CONCLUSIONS

1. Mathematical modeling is an integrated part of process development,

design and control in food industry

2. Analytical mathematical models based on mass and energy balances

for unit operations is the principal tool in process design and optimal

synthesis.

3. Mathematical models based on informatics (fuzzy logic and neural

networks) are applied for process control (robotisation, computer

vision, expert systems ..)

4. Use of computer software for design and process control is essential.

39LITERATURE

1. Ž. Kurtanjek, “Matematičko modeliranje i vođenje procesa”, Lecture

notes, Faculty of Food Technology and Biotechnology, University of

Zagreb, 1995.

2. Ž. Kurtanjek, “Matematičko modeliranje procesa u prehrambenoj

industriji”, Lecture notes, Faculty of Food Technology, University of

Osijek, 2000.

4. “Super Pro Design v. 4.5”, Manual, INTELLIGEN, INC. , 2001.

3. “Aspen Plus 10.1”, Manual, Aspen Technology, Inc., Boston, MA,

USA, 2000.