A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

›› 2009, Vol. 17 ›› Issue (2): 241-250.

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A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

ZHAO Chao, SU Hongye, GU Yong, CHU Jian

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Received:2008-01-07 Revised:2008-12-24 Online:2009-04-28 Published:2009-04-28
Supported by:
Supported by the National Creative Research Groups Science Foundation of China (60421002);National Basic Research Program of China (2007CB714000)

A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

赵超, 苏宏业, 古勇, 褚建

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

通讯作者: SU Hongye,E-mail:hysu@iipc.zju.edu.cn
基金资助:
Supported by the National Creative Research Groups Science Foundation of China (60421002);National Basic Research Program of China (2007CB714000)

Abstract

Abstract: In this article,an approach for economic performance assessment of model predictive control(MPC) system is presented.The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in variance.The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction.Combining the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem.The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.

Key words: economic performance assessment, model predictive control, linear quadratic Gaussian benchmark, steady-state model based optimization

摘要： In this article,an approach for economic performance assessment of model predictive control(MPC) system is presented.The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in variance.The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction.Combining the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem.The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.

关键词: economic performance assessment, model predictive control, linear quadratic Gaussian benchmark, steady-state model based optimization

ZHAO Chao, SU Hongye, GU Yong, CHU Jian. A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application[J]. , 2009, 17(2): 241-250.

赵超, 苏宏业, 古勇, 褚建. A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application[J]. , 2009, 17(2): 241-250.

References

1 Bauer, M., Craig, I.K., “Economic assessment of advanced process control-A survey and framework”, J. Process Control, 18, 2-18 (2008).
2 Edgar, T.F., “Control and operations:When does controllability equal profitability?”, Comput. Chem. Eng., 29, 41-49 (2004).
3 Martin, G.D., Turpin, L.E., Cline, R.P., “Estimating control function benefits”, Hydrocarbon Process., 69, 68-73 (1991).
4 Muske, K.B., “Estimation the economic benefit from improved process control”, Ind. Eng. Chem. Res., 42, 4535-4544 (2003).
5 Zhou, Y., Forbes, J.F., “Determining controller benefits via probabilistic optimization”, Int. J. Adapt. Control Signal Process., 17, 553-568 (2003).
6 Xu, F., Huang, B., Akande, S., “Performance assessment of model predictive control for variability and constraint tuning”, Ind. Eng. Chem. Res., 46, 1208-1219 (2007).
7 Huang, B., Edgar, C.T., “Model validation for industrial model predictive control systems”, Chem. Eng. Sci., 55, 2315-2327 (2000).
8 Ko, B.S., Edgar, T.F., “Performance assessment of constrained model predictive control systems”, AIChE J., 47 (6), 1363-1371 (2001).
9 Boyd, S., Barratt, C., Linear Control Design, Prentice Hall, New Jersey (1991).
10 Huang, B., Shah, S.L., Performance Assessment of Control Loops:Theory and Applications, Springer, New York (1999).
11 Cutler, C.R., Perry, R.T., “Real time optimization with multivariable control is required to maximize profits”, Comput. Chem. Eng., 7, 663-667 (1983).
12 Contreras, J.L., Marlin, T.E., “Control design for increased profit”, Comput. Chem. Eng., 24, 267-272 (2000).
13 Loeblein, C., Perkins, J.D., “Structural design for on-line process optimization (1) Dynamic economics of MPC”, AIChE J., 45 (4), 1018-1029 (1999).
14 Babri, P.A., Bandoni, J.A., Barton, G.W., Romagnoli, J.A., “Back-off calculations in optimizing control:A dynamic approach”, Comput. Chem. Eng., 19, 699-708 (1995).
15 Figueroa, J.L., Babri, P.A., Bandoni, J.A., Romagnoli, J.A., “Economic impact of disturbance and uncertainty parameters in chemical process-A dynamic back-off analysis”, Comput. Chem. Eng., 20, 453-461 (1996).
16 Narraway, L.T., Perkins, J.D., Barton, G.W., “Interaction between process design and process control:Economic analysis of process dynamic”, J. Process Control., 1, 243-250 (1991).
17 Young, J.C.C., Swartz, C.L.E., Ross, R., “On the effects of constraints, economics and uncertain disturbances on dynamic operability assessment”, Comput. Chem. Eng., 20, 667-682 (1996).
18 Loeblein, C., Perkins, J.D., “Economic analysis of different structures of on-line process optimization systems”, Comput. Chem. Eng., 22, 1257-1269 (1998).
19 Huang, B., “Pragmatic approach towards assessment of control loop performance”, Int. J. Adapt. Control Signal Process., 17, 589-608 (2003).
20 Kassmann, D.E., Badgwell, T.A., “Robust steady state target calculation for model predictive control”, AIChE J., 46 (5), 1007-1024 (2000).
21 Ying, C.M., Joseph, B., “Performance and stability analysis of LP-MPC and QP-MPC cascade control system”, AIChE J., 45 (7), 1521-1534, (1999).
22 Bequette, W.B., Process Control:Modeling, Design and Simulation, Prentice Hall, New Jersy (2003).
23 Rossiter, J.A., Model-based Predictive Control:A Practical Approach, CRC Press, USA (2003).

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A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

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