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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
