- Email: [email protected]
Predictive Mould Level Control in a Continuous Steel Casting Line
Copyright © 1996 IFAC 13th Triennial World Congress, San Francisco, USA
7b-035
PREDICTIVE MOULD LEVEL CONTROL in a CONTINUOUS STEEL CASTING LINE Robin De Keyser
University of Gent. Department of Control Engineering and Automation Technologiepark-Zwijnaarde 9. 8-9052 GENT. BELGIUM E-mail: [email protected]
Abstract: The stability of the mould level in the continuous casting machine is one of the main parameters affecting the surface quality of the final flat products (sheets & plates) in a steel factory, Variations in the mould level should be reduced to only some mm for improved steel quality. a specification which is often hard to fulfill with current standard PI(D) control. This paper introduces results of applying model based predictive control to the mould level process, Kcywords: Steel industry, level control, modelling, identification, predictive control.
l. INTRODUCTION
People all over the world have been working on the mould level control problem during the last dccade, mainly because of its direct effect on quality and its resulting economic impact (Graebe et aI., 1994; B<>cher et aI., 1991; Lamant et aI., 1990, Kong et aI., 1992; Paiuk et aI., 1989; Kato & Yamasita, 1986; De Keyser, 1995). Steel producing companies around the world have repeatedly confirmed that substantial benefits are gained from accurate mould level control. The main disturbance on the mould level has a nearly periodic nature and seems to be typical for this kind of continuous casting lines, as is illustrated in many papers published by other researchers in this field, It is not yet well-known where this disturbance comes from and consequently people in hydraulic and mechanical engineering as well as electrical drives experts arc doing research in order to improve the casting process. In parallel, control engineers try to approach the problem by more advanced feedback control with bettcr disturbance rejection characteristics than single-loop PID.
Our approach has been the following. First an extensive modelling project led to preliminary insight in the process fysical behaviour and resulted in a computer simulation of a casting line. The modclling was done with measurement data obtained from a real casting line during many measurement campaigns and experiments, The results have been published previously (Kong et aI., 1992). The computer simulation of the casting line has been intensively used to deisgn and test advanced control strategies before implementing thcm in the real-life casting line. The following control methods were compared (De Keyser, 1995) : 10 The standard single-loop PID-type controller which has been used up to now in most real-life casting lines; 2° A model based prediction control strategy which gave excellent disturbance rejection results, at least on simulation, but which needs a process model and leads to more complicated control software; 3 0 A multi-loop strategy consisting of 2 PID-type controllers operating in a master-slave configuration.
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to identify and that using a single model to describe the process is an unrealistic approach. However using a model whieh is more or less adapted LO the specific operating condition leads to significant improvements in the mould level variation.
6. REFERENCES Bocher, G., R. Obermann, B. Winklcr, G. Krtiger and P. Patte (1991). Slab quality improvement by means of advanced mould level control. In : 1st European Conference on Continuous Casting, Florence, Italy. Clarke, D.W., C. Mohtadi and P. Tuffs (1987). Generalized Predictive Control. Automatica, 23 (2), 137-160. De Keyser, R.M.C., A.R. Van Cauwenberghe (1985). EPSAC : Extended Prediction Self-Adaptive Control. In: Proc. lFAC Symposium on Identification, York, U.K. De Keyser, R.M.C. (1991). Basic Principles of Model Based Predictive Control. In : Proceedings European Control Conference ECC'9! , G renoble, France, 17531758. De Keyser, R.M.C. (1995). Improved Mould Level Control in Continuous Steel Casting Line. In: IF A C Symposium on Automation in Mining, Mineral and Metal Processing (1.1. Barker (Eel.)), IFAC MMM'95, South-Africa, 127-132. Graebe, S.F., G.C. Goodwin, M.R. West and P. Stepien (1994). An application of advanced control to steel casting. In : Proceedings 3rd IEEE Conference on Control Applications, Glasgow, U.K., 1533-153X. Kato H., M. Yamasita (19X6). New automation and control technology of slab caster, In : IF AC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'86), Tokyo, Japan, 2S3-25X. Kong, F., R. De Keyser, C. Martien, D. Verhasselt (1992). Model identification for !.he mould level control loop in a continuous casting machine. In : Proceedings 7th IFAC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'92), Beijing, China, 316-321. Lamant, J.Y" M. Larrecq, A. Mouchette, Y. Codur, 1. Gancarz and A. LecIercq (1990). Advanced control of mould operation and improved slab surface quality on Sollac continuous casters. In : Proceedings 6th International Iron and S/cel ConJ.!,fcss, Nagoya, Japan, 317-324.
Paiuk, l., A. Zanini, M. Remorino and O. Frola (1989). The automatic mould level control for a continuous casting process. lFAC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'89), Buenos Aires, Argentina, 205-208.
APPENDIX: Details of EPSAC algorithm The following predietor model was used:
A(q-I)y(t) =
C( -I) q u(t)+ q e(t) F(q-I) D(q-I) B( -I)
with - t : discrete time index - q-I : backward shin operator - yet) : measured mould level (fig. 3) - u(t) : desired stopper position (fig. 3) - e(t) : uncorrelated noise (0,0';) _ A(q-I) = (I_q-I) - B(q-I) / F(q-I) = identified process model The design polynomial D(q-l) should take care of the nonzero (and slowly varying) mean value of the mould outflow (fig. 3). It also increases robustness against lowfrequency errors (steps, drift disturbances). The C(q-l) polynomial takes care of high-frequency errors (noise and process model errors). It should be designed in order to trade ofT between performance and robustness. The optimal control vector u(l+k), k = 0 ... 9 is computed at each sampling instant t in order to minimize the multistep cost index 10
I[w(t+k)-y(t+k)t k=1
with GPC control horizon Nu = 1 (i.e. the usual structuration of the postulated future control input, being ~u(t+k) = 0, k > 0). This leads to a simple scalar solution.
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7b-035
PREDICTIVE MOULD LEVEL CONTROL in a CONTINUOUS STEEL CASTING LINE Robin De Keyser
University of Gent. Department of Control Engineering and Automation Technologiepark-Zwijnaarde 9. 8-9052 GENT. BELGIUM E-mail: [email protected]
Abstract: The stability of the mould level in the continuous casting machine is one of the main parameters affecting the surface quality of the final flat products (sheets & plates) in a steel factory, Variations in the mould level should be reduced to only some mm for improved steel quality. a specification which is often hard to fulfill with current standard PI(D) control. This paper introduces results of applying model based predictive control to the mould level process, Kcywords: Steel industry, level control, modelling, identification, predictive control.
l. INTRODUCTION
People all over the world have been working on the mould level control problem during the last dccade, mainly because of its direct effect on quality and its resulting economic impact (Graebe et aI., 1994; B<>cher et aI., 1991; Lamant et aI., 1990, Kong et aI., 1992; Paiuk et aI., 1989; Kato & Yamasita, 1986; De Keyser, 1995). Steel producing companies around the world have repeatedly confirmed that substantial benefits are gained from accurate mould level control. The main disturbance on the mould level has a nearly periodic nature and seems to be typical for this kind of continuous casting lines, as is illustrated in many papers published by other researchers in this field, It is not yet well-known where this disturbance comes from and consequently people in hydraulic and mechanical engineering as well as electrical drives experts arc doing research in order to improve the casting process. In parallel, control engineers try to approach the problem by more advanced feedback control with bettcr disturbance rejection characteristics than single-loop PID.
Our approach has been the following. First an extensive modelling project led to preliminary insight in the process fysical behaviour and resulted in a computer simulation of a casting line. The modclling was done with measurement data obtained from a real casting line during many measurement campaigns and experiments, The results have been published previously (Kong et aI., 1992). The computer simulation of the casting line has been intensively used to deisgn and test advanced control strategies before implementing thcm in the real-life casting line. The following control methods were compared (De Keyser, 1995) : 10 The standard single-loop PID-type controller which has been used up to now in most real-life casting lines; 2° A model based prediction control strategy which gave excellent disturbance rejection results, at least on simulation, but which needs a process model and leads to more complicated control software; 3 0 A multi-loop strategy consisting of 2 PID-type controllers operating in a master-slave configuration.
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to identify and that using a single model to describe the process is an unrealistic approach. However using a model whieh is more or less adapted LO the specific operating condition leads to significant improvements in the mould level variation.
6. REFERENCES Bocher, G., R. Obermann, B. Winklcr, G. Krtiger and P. Patte (1991). Slab quality improvement by means of advanced mould level control. In : 1st European Conference on Continuous Casting, Florence, Italy. Clarke, D.W., C. Mohtadi and P. Tuffs (1987). Generalized Predictive Control. Automatica, 23 (2), 137-160. De Keyser, R.M.C., A.R. Van Cauwenberghe (1985). EPSAC : Extended Prediction Self-Adaptive Control. In: Proc. lFAC Symposium on Identification, York, U.K. De Keyser, R.M.C. (1991). Basic Principles of Model Based Predictive Control. In : Proceedings European Control Conference ECC'9! , G renoble, France, 17531758. De Keyser, R.M.C. (1995). Improved Mould Level Control in Continuous Steel Casting Line. In: IF A C Symposium on Automation in Mining, Mineral and Metal Processing (1.1. Barker (Eel.)), IFAC MMM'95, South-Africa, 127-132. Graebe, S.F., G.C. Goodwin, M.R. West and P. Stepien (1994). An application of advanced control to steel casting. In : Proceedings 3rd IEEE Conference on Control Applications, Glasgow, U.K., 1533-153X. Kato H., M. Yamasita (19X6). New automation and control technology of slab caster, In : IF AC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'86), Tokyo, Japan, 2S3-25X. Kong, F., R. De Keyser, C. Martien, D. Verhasselt (1992). Model identification for !.he mould level control loop in a continuous casting machine. In : Proceedings 7th IFAC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'92), Beijing, China, 316-321. Lamant, J.Y" M. Larrecq, A. Mouchette, Y. Codur, 1. Gancarz and A. LecIercq (1990). Advanced control of mould operation and improved slab surface quality on Sollac continuous casters. In : Proceedings 6th International Iron and S/cel ConJ.!,fcss, Nagoya, Japan, 317-324.
Paiuk, l., A. Zanini, M. Remorino and O. Frola (1989). The automatic mould level control for a continuous casting process. lFAC Symposium on Automation in Mining, Mineral and Metal Processing (MMM'89), Buenos Aires, Argentina, 205-208.
APPENDIX: Details of EPSAC algorithm The following predietor model was used:
A(q-I)y(t) =
C( -I) q u(t)+ q e(t) F(q-I) D(q-I) B( -I)
with - t : discrete time index - q-I : backward shin operator - yet) : measured mould level (fig. 3) - u(t) : desired stopper position (fig. 3) - e(t) : uncorrelated noise (0,0';) _ A(q-I) = (I_q-I) - B(q-I) / F(q-I) = identified process model The design polynomial D(q-l) should take care of the nonzero (and slowly varying) mean value of the mould outflow (fig. 3). It also increases robustness against lowfrequency errors (steps, drift disturbances). The C(q-l) polynomial takes care of high-frequency errors (noise and process model errors). It should be designed in order to trade ofT between performance and robustness. The optimal control vector u(l+k), k = 0 ... 9 is computed at each sampling instant t in order to minimize the multistep cost index 10
I[w(t+k)-y(t+k)t k=1
with GPC control horizon Nu = 1 (i.e. the usual structuration of the postulated future control input, being ~u(t+k) = 0, k > 0). This leads to a simple scalar solution.
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