Modelling and Simulation at PTS J. Kappen PTS Munich

Modelling and Simulation at PTS

J. Kappen PTS Munichwww.ptspaper.de

© PTS Munich

Agenda

The PTS simulation approach Use of IDEAS in paper mill optimisation projects

– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object

Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry

A look into the future European networking activities – COST E36

© PTS Munich

Agenda





© PTS Munich

PTS Simulation Activities

Simulation activities since the late 80’ies PTS performs simulation activities in the field of

– Stock preparation– Wet End Chemistry– Coating– Water loop optimisation

Annual number of customer projects using simulation techniques– Approx. 15

Current number of research projects using simulation techniques– Approx. 15

© PTS Munich

IDEAS in PTS

Working with IDEAS since: 1998 Licences:

– 3 Network GOLD– 1 KODIAK– 1 Paper machine Library– 1 Pulp Library– 1 Advanced Control

Number of staff trained on IDEAS– 11

Other software packages in use– Matlab– Simulink– DOE: Modde

© PTS Munich

The PTS Approach on Simulation

PTS develops and commercializes a simulation supported process optimization product line:

PTS develops a product optimization tool to cut product cost and/or design superior product qualities:

C AP D

®

®

®

®

© PTS Munich

Agenda





© PTS Munich

Aspects of a Well Designed Water Circuit

stock preparation

paper machine

white water

tertiary circuit 1-5 m³/t

secondary circuit10-50 m³/t

primary circuit100-200 m³/t

fresh water

evaporation

fresh water plant

white water

clear waterclear water

recycled effluent

effluent treatment plant

waste

save all

effluent toreceiving waters

fibers

product

stock preparation

paper machine

white water

tertiary circuit 1-5 m³/t

secondary circuit10-50 m³/t

primary circuit100-200 m³/t

fresh water

evaporation

fresh water plant

white water

clear waterclear water

recycled effluent

effluent treatment plant

waste

save all

effluent toreceiving waters

fibers

product

no smell

low fiber and filler loss

low & constant contaminant loadings

no chemicals loss into wwtp

minimum variation of effluent flow

no water shortage at any time

low chemicals use

good circuit waterquality

minimum specific effluent volume

clean fibre stock

No additionalheating

minimum specific effluent volume

low & constant contaminant loadings

minimum variation of effluent flow

clean fibre stock

no water shortage at any time

No additionalheating

© PTS Munich

Key Problems in Mill Water Systems to be Solved by Simulation

What will happen if I reduce my specific effluent volume?

Is a loop separation beneficial to my process? How big should an integrated biological treatment

unit be? What will happen to the non degradable compounds

in the circuit water? How can heat be transferred to the paper machine? How much do I have to enlarge the water tanks in

the mill? What type of controls layout in the water circuits is

needed to smoothen the effluent flow trend?

© PTS Munich

Object: Loop

Status of the object– fully developed– steady state – fully coded into IDEAS

Operational features– ideal mixer– operation in pull or push mode possible– 1 pulp and 5 water/pulp inputs (1 draws in pull mode)– 1 pulp, 1 reject and 6 water output (1 overflow in push

mode; quality can be determined)– specific- and absolute heat in-and output, dissipation– component sources and sinks– “GUI” can be customized

© PTS Munich

Improved project performance through

-> reliabale methodology

-> simple model built

-> low effort for calibration

PTS Development: Loop Object

© PTS Munich

parameters:

water, solids, COD,

Ca, Cl, SO4, temperature

load input

freshwater

effluent

rejects

structure

of a typical

model

loop object

IDEAS Loop Model

© PTS Munich

Agenda





© PTS Munich

Prognosis of the impact of water system optimisation

Mill: MD Albbruck, Germany (LWC, MWC)

Application: Heat management and water loop setup optimisation

Example of a Paper Mill Heat Balance Optimisation

© PTS Munich

fresh water

WT 6 WT 5

NS DS

NSWasserturm

press

mech. fibertower

Oxi. Blt 1AOxi. Blt 1B

press

Red. Blt 6Red. Blt 4

presspress

PM 6PM 7 PM 5

fresh water (sealing water)

effluentfresh water (sealing)

warm fresh water

fresh water (heated)

clear waterPM6

effluent

effluent

clear water PM7clear water PM6

effluent

effluent

fresh water

Cricuit water

wood

SFWT 7WT 6

(kalt)

(heated) (heated)(cold)

press

WT 1

WT 1

fresh water

WT 7

WT 5

Red. Blt 5

clear water PM5

effluenteffluent

SF

Optimisation: Loop Separation and Heat Exchange

© PTS Munich

Model was built based on loop object Calibration using mill data Concept development Initial simulation runs to support discussion on concept

ideas Decision in favour of one concept taken Detailed simulation calculations:

- Balances for heat, COD and complexing agents

- Dimensioning of heat exchangers

Installation of heat exchangers Commissioning done by joint team MD and PTS

Project Steps

© PTS Munich

Improved separation of refining and bleaching department. Increase of temperature in reductive bleaching from 44°C

to 66°C as calculated. Increase of temperature in white water 42°C to 50°C as

calculated. Increased of whiteness by 0,5 - 1 point after bleaching. Peroxyde and hydrosulfite consumption reduced. Total cost for bleaching reduced by 7 to 12%, depending

on final whiteness. Significant gain in production rate as expected. Payback time < 0,7 years

Achieved Results

© PTS Munich

Results: Bleaching Stage

Peroxide dosage per ton

Bri

gh

tnes

s g

ain

before

after

© PTS Munich

Agenda





© PTS Munich

Statement 1: V(stock) = V(water buffer)

Statement 2: V(water dyn.) / V(stock dyn.)

= f (specific effluent volume)

Statement 3: To precisely define the buffer requirement, dynamic simulation calculations are

needed

0,01

0,1

1

10

0 5 10 15 20 25 30

Specific effluent volume [l/kg]

Dyn

amic

wa

ter

volu

mes

/

Dyn

amic

sto

ck v

olum

es

V

Actual situation

Approaches to the Dimensioning of Buffer Tanks

© PTS Munich

2

4

6

8

10

12

050100150200250

0:00

0:30

1:00

1:30

2:00

max min

buffer residence time(hrs.)

additionalwastewater volume

(l/kg)

volume of buffer m³

Dimensioning of Buffer Tanks

© PTS Munich

Hybrid modelling based upon:- Loop objects

- Targeted and other (relevant) tanks

- Data inputs (machine performance over time etc.)

High fidelity modelling only were required

Dynamic Models: Simulation Approach

© PTS Munich

Dynamic optimisation of in-mill water systems

Mill: Testliner

Application: Design of effluent flow control

Example of Designing an Effluent Flow Control

© PTS Munich

0,0

100,0

200,0

300,0

400,0

500,0

600,0

25.03.03 00:00 25.03.03 12:00 26.03.03 00:00 26.03.03 12:00 27.03.03 00:00

Basismodellohne geregelte Ausschleusung von Abwassermit geregelter Ausschleusung von Abwasser ohne Purgatormit geregelter Ausschleusung von Abwasser mit Purgator

1

2

3

4

scenario standard deviation1 as is situation 702 reduced specific effluent volume 1053 as 2 but with controlled effluent flow 504 as 3 but one water buffer tank taken out 35

Comparison of Various Options to Perform Effluent Flow Control

© PTS Munich

Dynamic optimisation of in-mill water systems

Mill: LWC

Application: Tank dimensioning in a SGW department

Example for the Dimensioning of Buffer Tanks

© PTS Munich

dynamic stock volume: 3200 m³

dynamic water volume: 550 m³

Required additional

chest volume:

Statement 1: V(stock) = V(water buffer) 2650 m³

Statement 2: V(water dyn.) / V(stock dyn) 900 m³

= f (specific effl. volume)

Statement 3: To precisely define the buffer 0 m³

requirement, dynamic simulation

calculations are needed

Dimensioning of Buffer Tanks

© PTS Munich

0

0,2

0,4

0,6

0,8

1

1,2

1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00

time [h]

pig

me

nt

co

nc

en

tra

tio

n [

%] 3

1

2

3

grade change

colour 1

colour 2

1 without emptying2 emptying stock preparation3 additional cleaning of papermachine

Model Upgrading: Simulation of Grade Change Dynamics

© PTS Munich

Fuzzy Control of Ideas Model

Ideas Process Model

Matlab Fuzzy Toolbox

Process Data

Control Parameters

© PTS Munich

Function of Matlab Link

Status of the object– fully developed– fully coded into IDEAS

Operational featuresAt start of Ideas Model: – Ideas opens Matlab and runs the initialization script.

Ideas is used to predefine Matlab workspace parameters.

During Simulation for every simulation step: – Output matrix is transmitted to Matlab workspace.– Simulation script is started within Matlab to perform

calculations necessary.– Input Matrix is taken back to Ideas.

© PTS Munich

Matlab Interface (Screenshot and Dialog Boxes)

MATLAB

© PTS Munich

Agenda





© PTS Munich

Adequate removal of detrimental substances

Minimum wear and tear

Optimally matched process stages

Low energy and chemicals

consumption

Properly dimensioned stock and water chests

Low stickies and ink content of the

finished stock

Finished stock with high product suitability

Minimum fibre losses

Controlled furnishOptimum deashing

Reliable HD cleaningReliable process

control

Appropriate reject and water management

Critical Performance Criteria of a Stock Preparation Plant

© PTS Munich

A Holistic Approach to Optimize Stock Preparation Plants

Concept development

Concept development

SimulationSimulation

Action planAction plan

CircuitryCircuitry

SamplingSampling

AnalyticsAnalytics

VolumesVolumes

Automation conceptAutomation concept

Process parameterProcess parameter BalancesBalances

Cost-Benefit analysisCost-Benefit analysis

BenchmarksBenchmarks

Sankey-DiagramsSankey-Diagrams

ProfilesProfiles

FlowsFlows

Efficiency valuesEfficiency values

ModellingModelling

Fibres/Fillers/additvesFibres/Fillers/additves

Production dataProduction data

Stickies controlStickies control

RejectsRejects

Equipment dataEquipment data

Circuitry evaluationCircuitry evaluation

ToolsData

acquisition

Modules

Yield improvementYield improvement

Deinking / BrightnessDeinking / Brightness

FaserpotentialFaserpotential

Cost-Benefit Calculation

Cost-Benefit Calculation

Basis

Benchmarking

Cost reduction

Capacity analysis

Basis

Benchmarking

Cost reduction

Capacity analysis

Equipment evaluationEquipment evaluation

®

© PTS Munich

Balancing of Stickies

The key issue is: to pay less attention on the stickies loading

but to look more in detail at the stickies load:

(stickies loading x mass flow (fibres + ash) = stickies load)

The load is what the paper machine gets

To know were the stickies load (expressed in m²/h) is going within the stock preparation helps to find out at which location it is most effective to start optimising the plant.

)1(min²

min²106 Equationmkgkg

mm

© PTS Munich

Solids and Stickies Balance - Coarse Screening

5,2 m²/min

1,0 m²/min

Fibersorters 1A and 1B

1. Stage

6,3 m²/min

688 kg/min

solids flow [kg/min]

macro-stickies load (INGEDE Method 4) [m²/min]

Dumpchest

Fibersorter2. Stage

Omnifractorchest

Rejectsorter

RejectFine screening

3rd stage

Deflaker

1,1 m²/min

146 kg/min

21 kg/min

4,1 m²/min

125 kg/min

542 kg/min

1,8 m²/min

33 kg/min

8,0 m²/min

700 kg/min

21 kg/min

0,2 m²/min

9 kg/min

2,5 m²/min

8,0 m²/min

6,3 m²/min

4,1 m²/min

2,5 m²/min

rejectfine screening

3rd stage

1,8 m²/min

© PTS Munich

Solids and Stickies Balance - Fine Screening

1,3 m 2/min

2 1,0 m /min

3. StageII 0,20 mm

1. Stage fine

screeningII 0,20 mm

8,0 m 2/min

Disperging

Fractionation

II 0,20 mm

2. Stage fine

screeningII 0,20 mm

700 kg/min

6,7 m 2/min

292 kg/min

408 kg/min

132 kg/min

160 kg/min

5,7 m 2/min

101 kg/min

1,4 m 2/min

59 kg/min

4,3 m 2/min

0,2 m 2/min

38 kg/min

2,6 m 2/min

271 kg/min

Long fibre

chest

solids flow [kg/min]

macro-stickies load (INGEDE Method 4) [m²/min]

short fibre

21 kg/min

4,1 m 2/min

to reject sorter coarse screening

4,1 m²/min

21 kg/min

to rejectsortercoarse screening

4,1 m²/min

to rejectsortercoarse screening

1,3 m²/min

1,4 m²/min

0,2 m²/min

1,0 m²/min

total3,9 m²/min

short fibre

long fibre

8,0 m²/min

© PTS Munich

Object: Sorter

Status of the object– Under development– Steady state– Hierarchical object

Operational features– Improved sorter object– Separation of 8 components– 5 options to calculate separation rates – Option to enter separation rates directly– Visibility of all calculated results– Integrated error display

© PTS Munich

Internal Dialogs of the Object Sorter

© PTS Munich

Selection of the Mode of Operation

Option Input valuesEquations to calculate

Separation rate E i,R

1 Ei,R, EWater,R -

2CA, CR, und S i,A

oder S i,R

3Qi, RW, and T or

CA oder C R

4 Qi, CA, C R

5 Qi, RV, C A, CR

6 Qi, RW, P

wIi

RiRi R

S

SE

,

,,

Wii

WR,i

RQQ1

RE

Wii

WR,i

RQQ1

RE

iV

A

RVi

A

RV

R,i

Q1RC

CRQ1

C

CR

E

Wii

WR,i

RQQ1

RE

© PTS Munich

event

Discrete/Cont.Executive

ERRORS

Cp

IDEAS 3.0.0 IDEAS MP-1

m/hfor

IDEAS MP-1

R

A

I

W

DS A

Display

Master

AA

KW 1

FB & P

R

AI

DR

PS

PS

R

AA

I

W

VS

DR Rejekt

VS Rejekt

PS Rejekt2

MM

PP

RR11

22

BK

SRP1 BK

SRP2 BK

PS Rejekt1

AB = Ableerbütte

BT = Bleichturm

C = Contaminex

DR = Dickstoffreiniger

DSP = Doppelsiebpresse

DS = Dünnstoffschlitzsortierung

F = Fiberizer

FB = Förderband

FGB = Flotations-Gutstoffbehälter

FW = Frischwasser

GSB = Gutstoffbehälter

KF = Klarfiltrat

KW = Klarwasserbehälter

M = MERI

NF = Nachflotation

P = Pulper

PM = Papiermaschine

PS = Pulpsortierung

PW = Presswasser

S = Sortiertrommel

SchB = Schaumbehälter

SB = Schlammbehälter

SE = Schlammaufbereitung

SM = Schlamm-MERI

SRP = Schneckenrejektpresse

SSB = Spuckstoffbehälter

SSP = Schlammschneckenpresse

SF = Scheibenfilter

SP = Schneckenpresse

ST = Stapelturm

SZ = Scheibenzerfaserer

TF = Trubfiltrat

VB = Verdünnungsbütte

VFP = Primäre Vorflotation

VFS = Sekundäre Vorflotation

VS = Vorsortierung

VST = Vorseihtisch

VW = verdunstetes Wasser

WT = Wasserturm

ZB = Zwischenbütte

PS Rejekt2

SchB

DS B Rejekt

DS B Rejekt

PP

SS

11

22

RR

DS Rejekt

DS Rejekt

P KW2

VF Rejekt

MM

PP

RR11

22

BK

R

A

I

W

DS B

PP

SS

11

22

RR

M

SP

DR Rejekt

MM

PP

RR11

22

Schlamm

MM

PP

RR11

22

MM

PP

RR11

22

BK

VF Rejekt

R

A

SM

PS Rejekt1

PM_ARA

KF

I

TF

SF 1

MM

PP

RR11

22

BKC1C1

C2C2PP

11

22

VSTSSA BK

MM

PP

RR11

22

BK

C1C1

C2C2PP

11

22

SSP

C1C1

C2C2PP

11

22

SRP 1MM

PP

RR11

22

VS Rejekt

RA

T

SA

SB

VFP A & B

NF Rejekt

R SA

SB

VFS A & B

C1C1

C2C2PP

11

22

SRP 2

SB 1

SB 2

PP

SS

11

22

RR

SRP1 BK

SRP2 BK

SSA BK

C1C1

C2C2PP

11

22

SSA 2

Container(Deponieabfall)

MM

PP

RR11

22

BKP

ARA

M Flotat

MM

PP

RR11

22

BK

Example Model Built with Sorter Object

AcceptOutAcceptOut

Con1InCon1In Rejekt1OutRejekt1Out Rejekt2OutRejekt2Out

R

A

I

F

R

A

I

C

R

AI

S

MM

PP

RR11

22

Con1InCon1In

ConAOutConAOut

ConROutConROutR

A

I

DS 3R

A

I

DS 1

R

A

I

DS 2

MM

PP

RR11

22

112233

PP

55

66

77

88

44 112233

PP

55

66

77

88

44

ConTFInConTFIn

© PTS Munich

Example: Optimization of Van Houtum Mill

One week of process analysis produced a clear evaluation of the actual situation:

- Efficiency of stock preparation relating to macro stickies separation was only about 40%

- desired value: 80 - 90%. Concept development: Small modifications and

operational changes Simulated separation rate in screening stage 70% Concept applied Achieved separation rate: 70%

© PTS Munich

Agenda





© PTS Munich

0

500

1000

1500

2000

2500

3000

0:00 0:30 1:00 1:30 2:00

00,51

1,522,53

3,544,5

Startup

0

10

20

30

40

50

60

70

80

90

100

0:00 0:30 1:00 1:30 2:00

Lev

el i

n %

AP 32

AP 12

Bütte 1

Bütte 2

MB DE

MB UL

Gbb

AS-Bütte

Puffer

process

data

simulated

data

Startup

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

90,0%

100,0%

20:00 20:30 21:00 21:30 22:00 22:30

Lev

el i

n %

AP 32

AP 12

Bütte 1

Bütte 2

MB DE

MB UL

Gbb

AS-Bütte

Puffer

Start up: Comparison of Simulated and Original Data

© PTS Munich

Evaluation of Dynamic Simulation Models – Software Development

Objective:Development of a MATLAB based software tool, suitable for evaluation of the forecast accuracy of a simulation model

Status of the object–Under development

Operational features–Data logging in Ideas (with Matlab Link)–Analysis of time trends (Matlab)–Scenario management (Matlab)–Automatic evaluation of new calculated scenarios (Matlab)

© PTS Munich

Evaluation of Dynamic Simulation Models – Screenshot

Under development

© PTS Munich

Agenda





© PTS Munich

Modelling the Properties of Paper: CAPD

Paper & board Fibres Process Who Target

Purchase Detecting cost-effective fibres

Paper mill Controlling process conditions

R & DDesign of innovative paper & board property combinations

fix

fix flow

flow

flow flow

?

?

?

Fibres & materials

Process parameters

Physical paper & board properties

C A

P D

A software-based modular tool box for pulp and paper enterprises that helps to predict variations in resulting physical

paper properties based on fibre characteristics and process parameters.

Optimisation under constraints

Reverse engineering

© PTS Munich

The Dependency Tree - a Kind of Neural Network

FLD dfSR fP

RHO

WSR SR0fRho

AVLINV

OG

SSA

H

RBA

FZFBA

C Z

P

b

BL

FWD

EPS

fD

WF WP

TI

Fibre char. Fibre and network constants Paper propertiesFibre Length Distrib. (FLD), Fibre Width Distr. (FWD), SR-

value (SR)

App. Density (RHO), Strain to rupture (EPS), Breaking length (BL), Tear index (TI)

FF

Coarseness (C), Shear strength (b), Zero span tensile (Z), Fibre wall density (W), SR0, Fibre strain (F), Fibre shear modul (F),

Packaging (fRho, fP), Dewatering (fSR), Statistic of failure (fD), Bonding distance (d)

WRV

© PTS Munich

CAPD: Simulation of Paper Sheet Strength Properties

727 743 759 775 791 808 824 840 856 872 88820

25

30

35

40

45

50

55

60

65

70

[g/cm3]

Av. Fibre length [m]

SR [o]

Apperent density

0.75-0.80

0.70-0.75

0.65-0.70

0.60-0.65

0.55-0.60

0.50-0.55

727 743 759 775 791 808 824 840 856 872 88820

25

30

35

40

45

50

55

60

65

70

[m]

Av. Fibre length [mm]

SR [o]

Breaking length

7000-7500

6500-7000

6000-6500

727 743 759 775 791 808 824 840 856 872 88820

25

30

35

40

45

50

55

60

65

70

[%]


SR [o]

Strain to rupture

3.75-4.00

3.50-3.75

3.25-3.50

3.00-3.25

727 743 759 775 791 808 824 840 856 872 88820

25

30

35

40

45

50

55

60

65

70

[mNm2/g]


SR [o]

Tear Index (E.)

11.50-12.00

11.00-11.50

10.50-11.00

10.00-10.50

9.50-10.00

9.00-9.50

8.50-9.00

8.00-8.50

Refining path

© PTS Munich

Agenda





© PTS Munich

Example Project to Model the Wet End Chemistry

Extensive paper machine trials– Online data vs. laboratory results (A60 value/Cobb60)

Correlation analyses of inputs and outputsCreation of a model for individual trial / overall

modelimportant parameters

– steam consumption after dryer section– moisture in front of the size press– consumption of sizing agent

Simulation results available as soft sensor Optimisation calculations can be used to minimize

cost for sizing (simulated annealing)

© PTS Munich

Correlation Map to Analyse Results of the Paper Machine Trials

Time Resin sizePAC Chalk KaolinWet end starch Retention agent Microparticles Broke treatment Starch solution Moisture before size press Grammage Moisture pope reel Ash Paper output Stean consumption Surface starch 1 Surface starch 2 Surface sizing Sizing Sizing factor Cobb water 60 top side Cobb water 60 wire side W top side Max top side A60 top side W web side Max web side A60 web side Poly Dadmac 0.001n white water Retention Resin size of paper Resin size of head box Resin size of white water

tp9p10p11p12p13p14p15p19p25p34p35p36p37p41p92p117p119p127p146p147p148p149p177p178p179p180p181p182p183p184p185p186p187

© PTS Munich

Optimisation of Sizing by Simulated Annealing

© PTS Munich

Agenda





© PTS Munich

A Look Into the Future

PTS will have available a tool to quantitatively evaluate the accuracy of dynamic models.

Relevance to the “IDEAS world”?

CAPD will, within the next years, be ready to be used for paper product development (virgin fiber based papers).

Should the results be integrated into Kodiak?

How do we get to accurate (physical) wet end chemistry models?

Will future IDEAS developments be supporting this?

© PTS Munich

Agenda





© PTS Munich

What is COST?

COST …

– is one of the oldest funding mechanisms of the European Commission

– has been established in order to promote the exchange of scientific knowledge within the European Community

– is a predecessor of the Networks of Excellence (NoE) defined within the 6th Framework Programme

– is funded by the European Community within the 6th Framework Programme and managed by the European Science Foundation (ESF)

© PTS Munich

Objectives and Benefits of Action E36

The main objective of the Action is to promote the development and application of modelling and simulation techniques in pulp and paper manufacturing processes.

The main benefit will be a better understanding of the mechanisms of the processes and their control loops. This will help to find solutions for currently pending problems in the paper industry: improving the paper quality, optimising the wet end chemistry, enhancing the runnability and reducing emissions by improving process design, process monitoring and decision support during operation.

In the long run this action should also contribute to

designing superior or new product properties.

© PTS Munich

Key Figures E36

13 participating countries: Austria, Belgium, Denmark, Finland, France, Germany, Netherlands, Norway, Slovakia, Slovenia, Spain, Sweden, United Kingdom

Members MC: 17

Members WG’s: > 40 (in 3 WG’s)

Duration: 4 years (22.1.2004 – 21.1.2008)

Budget: approx. 60 T€ per year

Estimated cost of the activities carried out in the course of the action: 20 M€ (180 person-years)

© PTS Munich

Activities

Action started with a kick off meeting in the beginning of 2004

Presentations given at the Munich conference in March 2004 provide a good insight into the simulation knowledge available in p&p

Young scientist exchange is financed and promoted by the action through short term scientific missions

Working groups are currently defining their thematic focus within the frame of the memorandum

Workshops and Conferences will be organised in order to further promote the knowledge exchange

Dedicated reports and books are currently elaborated and will be published soon

© PTS Munich

Working Groups

A Modelling and simulation of the pulping and the paper production processes (eg. R&D tool, optimal process design, operator training, trouble shooting)

B Online simulation of pulp and paper production processes (monitoring, operations decision support, model based control)

C Assessment of simulation software in the pulp and paper industry and recommendations on further developments (integration aspects included)

© PTS Munich

Working Group C: Specific Results

WG C will provide the following specific results– A survey on the current use of simulation software– Recommendations on the exchange of know how

contained in models– A report concerning recommendations on suitable

software tools and requirements for further software development

© PTS Munich

E 36: Whom to Contact

www.costE36.org

Chairman: Dr. Johannes Kappen (GE)([email protected])

Vice Chairman: Prof. Risto Ritala (FI)([email protected])

WG A leader: Dr. Jussi Manninen (FI)([email protected])

WG B leader: Prof. Erik Dahlquist (SE)([email protected])

WG C leader: Prof. Carlos Negro (ES)([email protected])

© PTS Munich

Summary

PTS has worked with IDEAS software successfully during the past years

IDEAS is our tool to support all process optimization projects

Thus IDEAS is the most important basis PTS has built its process optimization upon and will be in the future

Added value is included by combining IDEAS with Matlab.

In the long run PTS is interested in including these functionalities into IDEAS

PTS currently brings together European researchers in the field of simulation (COSTE36)

Documents

Modelling and Simulation at PTS J. Kappen PTS Munich