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CCC Annual ReportUIUC, August 19, 2015
Zhelin Chen, M.S. Student
Spray-Cooling Control for Maintaining
Metallurgical Length During Speed Drop
Department of Mechanical Science & Engineering
University of Illinois at Urbana-Champaign
Steelmaking Research Dept.
JFE Steel Corporation
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 2
Motivation
• Maintaining ML during speed changes within
certain bounds is helpful for many operations:
- Unbending to prevent cracks
- Staying inside support zone to prevent whales
- Soft reduction to prevent centerline segregation
• Objective: explore the potential to avoid
metallurgical length change during a casting
speed drop for a thick-slab caster using different
control methods.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 3
‘Whale’ Formation
• Ferrostatic pressure is transmitted from the meniscus via the
liquid pool acting internally on the steel shell.
• If ML exceeded the machine length, final portion of
solidifying steel is not supported by rolls, steel will bulge out.
From CCC
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 4
Soft Reduction
• In soft reduction
operation, roll gap
should match the
shrinkage to
prevent centerline
defects
• During speed
change, ML should
stay within soft
reduction region.
Rolls deform strand
to match shrinkage
Centerline is susceptible
to segregation and other
defects if the roll gap
does not satisfy the
desired shrinkage
Solid
Liquid
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 5
Objective
• Choose realistic caster and conditions to explore control methods to maintain ML during a speed drop
• Caster studied: thick slab caster at JFE Steel
• Steel grade: not sensitive to surface cracks but may be sensitive to centerline defects.
• Small speed drop: 1.7m/min to 1.5m/min.
- maintaining constant ML during steady state is
achievable by changing water flow rates.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 6
Cononline: On-line Control System for
Secondary Cooling Water Sprays
Concontroller: tries to keep
Consensor
predictions
at setpoints
Consensor:predicts
surface
temperature
and shell
thickness
[1] Petrus, B., K. Zheng, X. Zhou, B.G. Thomas, and J. Bentsman, “Real-Time Model-Based Spray-Cooling Control System for Steel Continuous Casting”, Metallurgical and Materials Transactions B, Vol. 42B:2, 87- 103, 2011
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 7
Conoffline
• Currently runs on one Linux server.
Recorded or specified casting
conditions
∑
SetpointGenerator
Surface temperature setpoint
Shell thickness setpoint
User specified control loop
Caster
data
Spray water
flow rate
Shell thickness and surface temperature estimation
Controller:• Concontroller: Automatic PI
control loop
• Spray table control
• “Time-constant” based control
• User specified control
methods
Consensor
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 8
Example of Conoffline “replay” File
• Generate scenarios by editing CSV files in Excel:
-- each row contains all (83) CON1D casting
conditions at specified time.
Time Casting conditions
Casting speed
sample_timedistance from meniscus to mold top
tundish nozzle submergence depth
speed width thicknessPouring
temp.Slab Chemistry
mm mm m/min mm mm deg_C
21:30:00 90 250 1.7 2095 221 1557
21:40:09 90 250 1.7 2095 221 1557
21:40:52 90 250 1.5 2095 221 1557
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 9
Validation of Cononline with Roll Force Measurements [4]
• Predicted thermal linear expansion of strand thickness (depends on liquid shrinkage & metallurgical length) matches closely with timing of changes in measured roll loads during speed changes at Burns Harbor caster [5].
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 10
Thermal Linear Expansion of Carbon Steels
Liquid data from: Jimbo & Cramb, Met. Trans. B, 24B, 1993, 5-10.Solid data from: Harste, Jablonka & Schwerdtfeger, 4th Int.Conf.
on Continuous Casting, CRM, 1988, Brussels, 633-644
1.5
2
2.5
3
3.5
4
1300 1350 1400 1450 1500 1550
TL
E (
%)
Temperature (C)
0.003%C
0.1%C
0.2%C
0.4%C
0.6%C
0.8%C
γγγγ or δδδδ
L
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 11
Casting Condition
Liquidus Temperature 1516.10 oC
Solidus Temperature 1468.37 oC
Density of solid steel 7400 kg/m3
Steel emissivity 0.8 -
Fraction solid for shell
thickness location0.3 -
Specific heat of solid steel 670 J/kgK
Thermal conductivity of solid
steel30 W/mK
Thermal diffusivity of solid steel 6.0508 e-6 m2/s
Latent heat of fusion 271 kJ/kg
Initial cooling water temperature 29.67 oC
Pour temperature 1545 oC
Mold conductivity (WF/NF) 418.7/355 W/mK
Time step 0.01 s
Mesh size 0.55 mm
Slab thickness 221 mm
Slab width 2095 mmSlab Size
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 12
Surface Heat Removal Eqs
• Mold heat flux: varies with casting speed, based
on empirical correlation for a thin slab caster [2]
• Heat flux due to water sprays: empirical relation
of Nozaki [3]
[ ]( )0.47
2
mMW/m 1.2154 m/min
cQ v =
( )
( )( )sw sw surf sw
0.55
sw sw surf sw0.3925 1 0.0075
q h T T
Q T T T
= −
= ⋅ ⋅ − ⋅ −
Average mold heat flux Casting speed
Spray water flux in L/m2/min
Steel surface
temperature Spray water
temperature
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 13
Shell Thickness at Steady State
0 1 2 3 4 5
x 104
0
20
40
60
80
100
120
Distance from meniscus(mm)
Shell
thic
kne
ss(m
m)
Casting speed: 1.7m/min
Casting speed: 1.5m/min
ML=22.29 m
ML=19.98 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 14
Calibration of Conoffline
• Calculated surface temperatures agree with measurements under steady state.
0 1 2 3 4 5
x 104
0
200
400
600
800
1000
1200
1400
1600
Distance from meniscus(mm)
Sla
b s
urf
ace
te
mp
era
ture
(de
gC
)
Vc=1.7m/min
Simulation result
Measured result
0 1 2 3 4 5
x 104
0
200
400
600
800
1000
1200
1400
1600
Distance from meniscus(mm)
Sla
b s
urf
ace t
em
pera
ture
(de
gC
)
Vc=1.5m/min
Simulation result
Measured value
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 15
• Control setpoints: Controller setpoints is generated based on the Cononline result of steady state at 1.7 m/min.
Zone # Setpoint (deg)
1 1214.62
2 1122.15
3 1071.99
4 932.26
5 840.31
6 795.21
7 771.47
8 780.88
9 822.50
10 816.52
11 792.28
12 726.04
Zone # Kp (/60)
1 0.5
2 2
3 2
4 2.5
5 2.5
6 3.5
7 3.5
8 3.5
9 2.5
10 2.5
11 4.5
12 4.5
Zone # Ki (/60)
1 0.1
2 0.1
3 0.25
4 0.25
5 0.25
6 0.25
7 0.25
8 0.25
9 0.25
10 0.25
11 0.35
12 0.35
First 2 zone is very short and right after the mold exit and their behavior will
largely affect the surface temperature for the following zones, so choose smaller
kp and smaller damping (ki)
Example Concontroller Setup for
Cononline/Conoffline PI Control
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 16
-500 0 500 1000 1500900
950
1000
1050
1100
1150
1200
1250
Time after speed change(Sec)
Avera
ge
Su
rfa
ce T
em
pera
ture
(de
g)
Average Surface Temperature Change During Sudden Slow Down
zone 1
zone 1 setpointzone 2
zone 2 setpoint
zone 3
zone 3 setpointzone 4
zone 4 setpoint
Simulation Results for PI Control:
Surface Temperature Histories (12 zones)
-500 0 500 1000 1500750
760
770
780
790
800
810
820
830
840
850
Time after speed change(Sec)
Avera
ge S
urf
ace T
em
pera
ture
(deg)
Average Surface Temperature Change During Sudden Slow Down
zone 5
zone 5 setpointzone 6
zone 6 setpoint
zone 7
zone 7 setpointzone 8
zone 8 setpoint
-500 0 500 1000 1500700
720
740
760
780
800
820
840
Time after speed change(Sec)
Avera
ge S
urf
ace T
em
pera
ture
(deg)
Average Surface Temperature Change During Sudden Slow Down
zone 9
zone 9 setpointzone 10
zone 10 setpoint
zone 11
zone 11 setpointzone 12
zone 12 setpoint
-1000 -500 0 500 1000 15000
20
40
60
80
100
120
140
Time after speed change(Sec)
Wate
r flow
rate
(l/m
in/r
ow
)
Spray Flow Rate Change During Sudden Slow Down
zone 1zone 2
zone 3
zone 4zone 5
zone 6
zone 7
zone 8zone 9
zone 10
zone 11zone 12
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 17
ML Behavior during Speed Change under
PI Control
Cononline can maintain constant surface temperature during speed change,
but its performance on maintaining ML is not good.
2.31 m
-500 -100 300 700 1100 1500 1900
1.5
1.6
1.7
Time(Sec)
Casting s
peed(m
/min
)
Metallurgical Length Change During Sudden Slow Down
-500 -100 300 700 1100 1500 190019
20
21
22
23
Meta
llurg
ical Length
(m)
2.66 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 18
Reduce water flow rates at lower speed: match ML at steady-state
0 1 2 3 4 5
x 104
600
700
800
900
1000
1100
Distance from meniscus(mm)
Sla
b s
urf
ace tem
pera
ture
(de
gC
)
1.7-Conv.1.5-Conv
1.5-Reduced
No. 1.7-Conv. 1.5-Conv. 1.5-Reduced.
vc 1.7 m/min 1.5 m/min 1.5 m/min
Spray pattern Conventional Conventional
1-2Z: Conv.
3Z: Conv. x 0.7
4Z: Conv. x 0.45
5-8Z: Conv. x 0.35
8-12Z: Conv. x 0.7
ML (fs=0.7) 22.29 m 19.98 m 22.17 m
0 1 2 3 4 5
x 104
0
20
40
60
80
100
120
Distance from meniscus(mm)
Sh
ell
thic
kn
ess (
mm
)
1.7-Conv.
1.5-Conv1.5-Reduced
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 19
Control Methods for ML in Transient Conditions (i.e. speed drop)
• Spray table control
- conventional (conv.) to conv.
- conv. to reduced
• Time-constant spray control
• PI control for ML (60X surf temp gains)
(1.7 to 1.5 m/min)
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 20
Spray Table Control (conv. to conv.)
-ML
• Spray pattern:
conventional (1.7m/min) to conventional (1.5 m/min)
• ML drop = 2.31 m
= ML change (defined as maximum change in ML)
2.31 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 21
Spray Sable Control (conv. to conv.)
-Surface Temperature
• Spray pattern = conventional
to conventional
zone1
zone2
zone3
zone4
zone6
zone5
zone8zone7
zone10
zone9
zone11
zone12
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 22
Spray Table Control (conv. to reduced) -ML
• Spray pattern = conventional to reduced
• ML change = 0.92 m
0.92 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 23
Spray Table Control (conv. to reduced)-Surface Temperature
• Spray pattern = conventional
to reduced
zone1zone2
zone3
zone4
zone6
zone5
zone7
zone8
zone10zone9
zone11
zone12
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 24
“Time-constant” Spray Control(conv. to reduced spray pattern)
• Spray table control: spray water rate changes immediately when speed changes.
• Intuitive idea: spray water rate changes gradually as speed changes, (i.e. spray water rate changes according to time instead of velocity).
• For a typical caster, the relation between spray water rate and casting speed is assumed to be:
• Above equation can be transferred to:
• Can be found solving the inverse of equation:
[ ] [ ]/ / / minspray cSW l m row A Bv m= +
[ ]( )
[ ]/ / / minspray
zSW l m row A B m
zτ= +
( )( )t
t Z v s ds zτ−∫ =
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 25
“Time-constant” Spray Control - ML
(conv. to reduced spray pattern)
• ML change = 1.61 m
1.61 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 26
“Time-constant” Spray Control – Surface
temperature (conv. to reduced spray pattern)
• The transition between two
steady state is more
smooth than conv. to
reduced spray table control
zone1zone2
zone3
zone4
zone6
zone5
zone7
zone8 zone10
zone9
zone11
zone12
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 27
• Control setpoints: Controller setpoints is generated based on the CONONLINE result of steady state at 1.7 m/min.
Zone # Setpoint (mm)
3 23.65
4 33.45
5 44.32
6 58.80
7 76.20
8 91.25
9 105.01
10 110.5
11 110.5
12 110.5
Zone #
Water flow rate upper bound (l/min/row)
3 180
4 70
5 70
6 63
7 63
8 42
9 26
10 48
11 36
12 36
Zone #
Water flow rate lower bound (l/min/row)
3 36
4 10
5 10
6 4.5
7 4.5
8 3
9 1.53
10 1.5
11 1.5
12 1.5
PI Control based on Shell Thickness
Setpoints
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 28
PI control based on Shell Thickness Setpoints-ML
• ML change = 0.87 m, BEST
-500 -100 300 700 1100 1500 1900
1.5
1.6
1.7
Time(Sec)
Casting s
peed(m
/min
)
Metallurgical Length Change During Sudden Slow Down
-500 -100 300 700 1100 1500 190019
20
21
22
23
24
25
X: -38
Y: 22.29
Meta
llurg
ical Length
(m)
0.87 m
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 29
PI Control based on Shell Thickness Setpoints - Ave. Surface Temp.
-500 0 500 1000 1500750
800
850
900
950
1000
1050
1100
1150
Time after speed change(Sec)
Ave
rag
e S
urf
ace
Te
mp
era
ture
(deg
)
Average Surface Temperature Change During Sudden Slow Down
zone 5
zone 6zone 7
zone 8
-500 0 500 1000 1500700
750
800
850
900
950
1000
1050
1100
Time after speed change(Sec)
Ave
rag
e S
urf
ace
Te
mp
era
ture
(deg
)
Average Surface Temperature Change During Sudden Slow Down
zone 9
zone 10zone 11
zone 12
-500 0 500 1000 1500900
950
1000
1050
1100
1150
1200
1250
Time after speed change(Sec)
Ave
rag
e S
urf
ace
Te
mp
era
ture
(deg
)
Average Surface Temperature Change During Sudden Slow Down
zone 1
zone 2zone 3
zone 4
zone1zone2
zone3zone4
zone6
zone5
zone7zone8
zone10
zone9
zone11
zone12
• Change of water flow rate have
immediate response on average
surface temperature.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
Time after speed change (sec)
Ave
. S
urf
. Te
mp
.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 30
Conclusions: Conoffline
• Has been calibrated to match steady casting conditions at a
commercial thick-slab caster
• Using PI controller, surface temperature can successfully be
controlled during a speed drop at this caster.
• Has been applied to investigate potential for ML control
during speed drop from 1.7 to 1.5 m/min:
– Conventional spray table control causes metallurgical length drop of 2.31 m (fs=0.7).
– Spray table control with conv. to reduced water flow rates at low speed causes metallurgical length (ML) drop of 0.92 m (fs=0.7).
– “Time constant” spray control (based on conv. to reduced spray table control) causes ML change of 1.61 m (fs=0.7)
– PI control based on shell thickness causes ML change of 0.87 m.
30
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 31
Future work
• Improve control methods to maintain ML during speed change.
• Tune the PI controller gains for shell thickness feed back control method.
• Find better (fundamental) methods for ML control
• Investigate other scenarios?
• Any other suggestions?
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 32
Acknowledgments
• Continuous Casting Consortium Members (ABB, AK Steel,
ArcelorMittal, Baosteel, JFE Steel Corp., Magnesita
Refractories, Nippon Steel and Sumitomo Metal Corp.,
Nucor Steel, Postech/ Posco, SSAB, ANSYS/ Fluent)
• JFE Steel for providing caster data and casting conditions
• Akitoshi Matsui (JFE Steel) for his excellent work and
continuous support on this project.
• Dr. Petrus, (former grad student, currently at Nucor) for
helping with Cononline program development and advice.
• Prof. Thomas and Prof. Bentsman for their patience,
knowledge and guidance on this project.
University of Illinois at Urbana-Champaign • Metals Processing Simulation Lab • Zhelin Chen • 33
References
• [1] Petrus, B., K. Zheng, X. Zhou, B.G. Thomas, and J. Bentsman, “Real-Time Model-Based Spray-Cooling Control System for Steel Continuous Casting”, Metallurgical and Materials Transactions B, Vol. 42B:2, 87- 103, 2011
• [2] P. Duvvuri, B. Petrus, B.G. Thomas, “Correlation for Mold Heat Flux Measured In A Thin Slab Casting Mold,” AISTech 2014, Indianapolis, IN, 2014
• [3] T. Nozaki: Iron Steel Inst. Jpn. Trans., vol. 18, pp. 330-38, 1978.
• [4] Petrus, Bryan, Danny Hammon, Megan Miller, Bob Williams, Adam Zewe, Zhelin Chen, Joseph Bentsman, Brian G. Thomas, “New Method to Measure Metallurgical Length and Application to Improve Computational Models”, AISTech 2015, Cleveland, OH, May 4-6, 2015, Assoc. Iron Steel Technology, Warrendale, PA, pp. 3238-3248.
• [5] Gregurich, N., Flick, G., Moravec, R., and Blazek, B. “In-Depth Analysis of Continuous Caster Machine Behavior During Casting With Different Roll Gap Taper Profiles”, Iron & Steel Technology, Dec. 2012