CONTINUOUS CASTING OF STEEL: MODELLING, SIMULATION, OPTIMISATION

CONTINUOUS CASTING OF STEEL:MODELLING, SIMULATION, OPTIMISATION,

EXPERIMENTSBo�idar �arler

Laboratory for Multiphase ProcessesNova Gorica Polytechnic, Nova Gorica, Slovenia

Institut Podstawowych Problemov TechnikiPolska Akademia NaukCentrum Doskonalosci

�Nowoczesne Materialy i Konstrukcje�Warszawa, Polska, kwiecien 23, 2003

Lecture notes: http://fluid.ippt.gov.pl/sarler

Jure Mencinger, Janez Perko, Robert Vertnik, Miha Zalo�nikLaboratory for Multiphase Processes, Nova Gorica Polytechnic, Nova Gorica, Slovenia

Gojko Manojlovič, Janko CesarTechnical Development, INEXA-�TORE, �tore, Slovenia

Ale� Lagoja, Emil �ubeljACRONI Jesenice Steelworks, Jesenice, Slovenia

Mojca Sabolič, Bojan Marčič, Igor JustinekATES Industrial Automatization

Bogdan FilipičDepartment of Intelligent Systems, Jo�ef Stefan Institute, Ljubljana, Slovenia

Miroslav Raudenský, Jaroslav HorskýHeat Transfer Laboratory, Techical University of Brno, Brno, Czech Republic

Errki LaitinenDepartment of Mathematical Sciences, Oulu University of Technology, Oulu, Finland

Collaborators

Materials

Steel approx. 1.5x as 20 years ago

Aluminium approx. 3.0x as 20 years ago

Polymers approx. 6.0x as 20 years ago

1000 mil.tons Fe30 mil.tons Al

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

CONTINUOUS CASTING OF STEEL

PROJECTS

CZ-SI (1999-2000) Modelling and Optimisation for Competitive CC - I

COST P3 (1997-2001) Simulation of Physical Phenomena in Technological Applications

COST 526 (2001-2004) Automatic Process Optimisation in Materials Technology

CZ-SI (2001-2002) Modelling and Optimisation for Competitive CC - II

INEXA (1997-2001) Modernisation of Billet CasterACRONI (1997-2001) Modernisation of Slab Caster

SM Education Science and Sport (1997-2001, 2003-2005)SM Trade (1997-2001)

Product:

correct shapeno cracksno porositydesired compositiondesired structure

Process:

safetyproductivity

PROCESS SCHEME

SPRAY SYSTEMS SCHEMATICSSPRAY SYSTEMS SCHEMATICS

NOZZLE P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 P12664.887.30.90 12 12 10664.847.30.90 10 10 10664.807.30.90 15660.807.30 10660.677.30 10660.727.30 36660.767.30 24660.766.30 8 8460.726.30.CE 8652.726.30 4SKUPAJ 8 20 36 24 12 12 10 10 10 10 15 20

Typical problems SURFACE CRACKS

INTERNAL CRACKS

SHAPE DEFECTS

POROSITY

SEGREGATION

PROCESS PARAMETERS ?

100steel gades*10formats*10process parameters = 10 000 settings

TRANSVERSAL CRACKS

LONGITUDINAL CRACKS

SHAPE DEFECTS

SHAPE DEFECTS

INCLUSIONS

POROSITY

MICROSEGREGATION

MACROSEGREGATION

SCOPE

CASTER INFORMATISATION STRATEGIES

CASTER CONTROL SYSTEM

SOLIDIFICATION PROCESS MODELS

PRESENTATION OF SIMULATION SYSTEM

CALCULATION OF REGULATION COEFFICIENTS

OPTIMISATION OF PROCESS PARAMETERS

CONCLUSIONS AND FURTHER DEVELOPMENTS

VALIDATION EXPERIMENTS

constructed in 1987 (Mannesmann-Demag)capacity 350.000 ton/yearslabs [80-160 cm]x[16, 20, 25 cm]

constructed in 1986 (Concast)capacity 150.000 ton/yearbillets [14, 18, 22 cm]2

CASTER INFORMATISATION STRATEGIES

J.K.Brimacombe (1991)

BETTER INSIGHT INTO THE PROCESS

system for data acquisition, monitoring, archiving, analysis of process results

BETTER UNDERSTANDING OF THE PROCESS

methods of experimental and numerical modelling

BETTER INFLUENCE ON THE PROCESS

caster automatisation

BETTER ORGANISATION OF THE WORK AROUND THE PROCESS

use of new informatisation technologies in planning, scheduling,...

PLC SIEMENS S7-300BILLET CASTER

NEW SENSORS

NEW ACTUATORS

BILLET CASTER INFORMATISATION SCHEMATICS

ON-LINE SYSTEMS

OFF-LINE SYSTEMS

INEXA-�TORE BILLET CASTER CONTROL SYSTEM

ACRONI-JESENICE SLAB CASTER CONTROL SYSTEM

SOLIDIFICATION MODELLING TASKS

M.Rappaz (1995)

SIMULATION OF INTERCONNECTIONS BETWEEN

process parameters and macrostructure

product macrostructure and microstructure

product microstructure and properties

process parameters

product properties

SOLIDIFICATION MODELLING TASKS

PROCESS MODELS

B.G. Thomas (1995)

ON-LINE MODELS � control system, regulation

PARTIAL ON-LINE MODELS � design of processparameters settings

OFF-LINE MODELS � caster design

LITERATURE MODELS � for research purposes 1997

2003

PARTIAL ON-LINE MODELS

INFORMATION ON BILLET TEMPERATURE FIELD

CALCULATION OF REGULATION PARAMETERS

OPTIMISATION OF PROCESS PARAMETERS

CASTER DESIGN CHANGES

HEAT TRANSFERMECHANISMS

Temperature field

Temperature + velocity

field

Temperature + velocity

+ macrosegragation

field

Temperature + velocity

+ macrosegregation field +

microstructure

scheme of modelling complexity

Schematics of the simulation systemSchematics of the simulation system

3 basic elements + automatic user upgrades

PARTIAL ONPARTIAL ON--LINE MODELLINE MODELSIMULATION RESULTS OVERVIEWSIMULATION RESULTS OVERVIEW

MAIN PROCESS PARAMETERS

� steel grade: WN1.1221C=0.61, Si=0.40, Mn=0.75, P=0.035, S=0.035, Cr=0.025, Ni=0.025, Mo=0.10

� billet format: 180[mm] x 180[mm]� casting temperature: 30[°C] above liquidus� casting speed: 1.1[m/min]� mold parameters: design� EMS: on� mold flow: normal� secondary cooling: design� radiation shield: on

Material Property: EnthalpyMaterial Property: Enthalpy

Material Property: Specific HeatMaterial Property: Specific Heat

Material Property: Thermal ConductivityMaterial Property: Thermal Conductivity

Material Property Material Property -- DensityDensity

Material Property Material Property -- Liquid Phase Volume FractionLiquid Phase Volume Fraction

Caster GeometryCaster Geometry

CenterlineCenterline TemperaturesTemperatures

Surface TemperaturesSurface Temperatures

Temperature Difference Temperature Difference -- Upper and Lower Upper and Lower CenterlineCenterline

Temperature Difference Temperature Difference -- Left and Right Left and Right CenterlineCenterline

Shell ThicknessShell Thickness

Average Solid FractionAverage Solid Fraction

CrossectionCrossection TemperaturesTemperatures

Crossection Crossection Phase FieldPhase Field

Crossection Crossection Temperature and Phase Fields Temperature and Phase Fields -- End of End of MoldMold

Crossection Crossection Temperature and Phase Fields Temperature and Phase Fields -- End of PreEnd of Pre--ShadingShading

Crossection Crossection Temperature and Phase Fields Temperature and Phase Fields -- End of Third Support SegmentEnd of Third Support Segment

CrossectionCrossection Surface Temperatures Surface Temperatures -- End of WreathEnd of Wreath

CrossectionCrossection Surface Temperatures Surface Temperatures -- End of Third Horizontal SegmentEnd of Third Horizontal Segment

CaseterCaseter Segments Temperatures Overview Segments Temperatures Overview -- CenterlineCenterline, Corner, Average , Corner, Average

Product:

correct shapeno cracksno porositydesired compositiondesired structure

Process:

safetyproductivity

PROCESS SCHEME

REGULATION ALGORITHM SCHEMATICS

COMPENSATE CHANGES IN CASTING TEMPERATURECOMPENSATE CHANGES IN MOLD HEAT EXTRACTION

BY

CHANGES IN CASTING SPEEDCHANGES IN SECONDARY COOLING FLOWS

FORMAT AND STEEL GRADE DEPENDENT TASK !

Casting Temperature Change Casting Temperature Change -- Nominal and +20[°C]Nominal and +20[°C]

Casting Temperature Change Casting Temperature Change -- Centerline Centerline Temperature DifferenceTemperature Difference

Casting Temperature Change Casting Temperature Change -- Average Liquid Fraction DifferenceAverage Liquid Fraction Difference

Casting Speed Change Casting Speed Change -- Nominal and +0.2[m/min]Nominal and +0.2[m/min]

Casting Speed Change Casting Speed Change -- Centerline Centerline Temperature DifferenceTemperature Difference

Casting Speed Change Casting Speed Change -- Average Liquid Fraction DifferenceAverage Liquid Fraction Difference

Regulation algorithm: metallurgical length as a function of casting temperature

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

-25 -20 -15 -10 -5 0 5 10 15 20 25

∆T [K]

∆M

L [m

]

Regulation algorithm: metallurgical length as a function of casting speed

-5

-4

-3

-2

-1

0

1

2

3

4

5

-0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4

∆v [m/min]

∆M

L [m

]

Windows Application for Running of the Simulator

Dynamic Simulation Input Editing Wizard

Simulation Input Editing Wizard

Windows Application for Running of the Plotting

Plotting Input Editing Wizard

Measurement Data Input Editing Wizard

Control signals

Process parameters

Scheme of the optimization

CONTROLSYSTEM

CASTINGDEVICE

�Steel grade�Strand dimensions�Metallurgical cooling criteria

PROCESSSIMULATOR

OPTIMISATIONPROCEDURE

How to find proper steady-state process parameter settings ?

Metallurgical cooling criteriaempirical !

SAFETY

QUALITY

ECONOMY

ECOLOGY

Metallurgical cooling criteria

� minimum shell thickness (end of mold)� maximum safety depth of liquid pool

SAFETY

Metallurgical cooling criteria

� maximum depth of liquid pool� maximum/minimum slab surface

cooling/reheating rate in secondary cooling zone

QUALITY

Metallurgical cooling criteria

� minimum slab surface temperature in unbending region

� maximum negative/positive strand surface temperature deviation at given axial position in secondary cooling zone

QUALITY

Metallurgical cooling criteria

� maximum casting speed� minimum superheat

ECONOMY

Metallurgical cooling criteria

� minimum water consumption

ECOLOGY

Evaluation of metallurgical cooling criteria

SIMULATOR

Evaluation of setting

OPTIMISATION TASK

OPTMISATION STRATEGY

Optimisation task� Find process parameter settings optimal with respect

to metallurgical cooling criteria

� Cost function:

∑−−

==

Nc

j jj

jjj cc

ccKf1 minmax

min

∑= −

−=

Nc

jjj

jjj cc

ccKf1

minmax

min

Nc � number of criteria

Kj � weight of criterion j

cj � value of criterion j

cjmin � minimum value of criterion j

cjmax � maximum value of criterion j

Optimisation task

� Basic optimization method: evolutionary algorithm

� Interaction with the process simulator via file data transfer

� Suitable for various types of parameters: continuous, discrete

� Allows for incorporation of problem-specific information when available

Optimisation strategy

Taxonomy

NeuralNetworks

EvolutionaryProgramming

EvolutionStrategies

GeneticAlgorithms

GeneticProgramming

EvolutionaryAlgorithms

FuzzySystems

COMPUTATIONALINTELLIGENCE

orSOFT COMPUTING

1. Generate initial set of process parameter settings.2. Evaluate the settings by simulating the casting process.3. Store parameter setting with minimum f as a result.4. Select a subset of settings with low f for further processing.5. Generate new parameter settings by exchanging

components among existing settings.6. Modify parameter settings by changing their components.7. Evaluate the settings by simulating the casting process.8. If f is decreased, store parameter setting with minimum f as

a result.9. If maximum number of iterations is reached, stop, otherwise

go to step 4.

Optimisation task

Parameter

Unit

Minimum value

Maximum value

Step size

Number of values

Casting temperature °C 1478.85 1488.85 2.5 5 Casting speed m/min 0.9 1.1 0.05 5 Spray coolant flow 1 l/min 110 150 10 5 Spray coolant flow 2 l/min 70 110 10 5 Spray coolant flow 3 l/min 190 270 10 9 Spray coolant flow 4 l/min 150 210 10 7 Spray coolant flow 5 l/min 95 135 10 5 Spray coolant flow 6 l/min 110 150 10 5 Spray coolant flow 7 l/min 65 85 10 3 Spray coolant flow 8 l/min 70 110 10 5 Spray coolant flow 9 l/min 55 75 10 3 Spray coolant flow 10 l/min 60 100 10 5 Spray coolant flow 11 l/min 50 70 10 3 Spray coolant flow 12 l/min 50 70 10 3

Optimisation - example

2*10+9

Parameter optimisation for steel AISI-304

1,0

2,0

3,0

4,0

0 1000 2000 3000 4000 5000

Process simulations

Cos

t fun

ctio

n

v=1,05 m/min

v=1,00 m/min

v=0,95 m/min

Manual setting

Parameter optimisation for steel AISI-304

1,0

2,0

3,0

4,0

0 1000 2000 3000 4000 5000

Process simulations

Cos

t fun

ctio

n

v=1,05 m/min

v=1,00 m/min

v=0,95 m/min

Manual setting

Further issues� Hybridization with local search (gradient-based)

techniques based on preliminary analysis of fitness landscapes

� Incorporation of knowledge-based operators

� Various sets of criteria, e.g. safety, quality, productivity

� Balancing among contradicting requirements, e.g. quality vs. productivity

COST 526

'Automatic Process Optimizationin Materials Technology'

(APOMAT)

The main objective of COST 526 is to develop and to apply numerical optimization methodologies for automatic materials process design, based on quantified product qualities, relating

to process targets and constraints, including economic aspects. Collaborative work is based on evaluated projects and

constitutes to a high degree on interdisciplinary cooperation between European materials engineers and optimization

experts of high reputation.

INFORMATISATION UPGRADEShigh effect/complexity ratiosecondary cooling design changescaster informatisation engineer

SIMULATORadditional plant and laboratory measurementsadditional systematic tuning of the correlationsadditional user friendly options220[mm] x 220[mm] billet upgrades

OPTIMIZATORsteel grade specific metallurgical optimisation criteriainteraction between safety, quality and economy criteriaincorporation of knowledge base operatorsoptimisation of numerical performance (from 103 to 102)

CONTROL SYSTEMquality management upgrades, online optimum control

INTERDISCIPLINARYINTERINSTITUTIONALINTERNATIONAL

numerical modelling and computer graphicsplant specific technology and expert knowledgeplant and laboratory measurementsindustrial automationoptimisation criteriaoptimisation procedures

INEXA-�TORE (SI), ACRONI (SI),NOVA HUT (CZ), RAAUTARRUUKI (FI)

CROSS-CHECKING OF CONCEPTS

SYNTHESIS OF EXPERT KNOWLEDGE

GENERALISATION OF THE RESULTS

CERTIFICATES OF QUALITY