Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

doi:10.1016/j.cjche.2014.05.013

›› 2014, Vol. 22 ›› Issue (7): 769-773.DOI: 10.1016/j.cjche.2014.05.013

• PROCESS CONTROL • 上一篇下一篇

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

Kai Li¹, Dewei Li¹, Yugeng Xi¹, Debin Yin ²

1. Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China;
2. Shanghai Xinhua Control Technology (Group) Co., Ltd, Shanghai 200241, China

收稿日期:2013-05-06 修回日期:2013-10-24 出版日期:2014-07-28 发布日期:2014-08-23
通讯作者: Dewei Li
基金资助:
Supported by the State Key Laboratory of Synthetical Automation for Process Industries, the National Natural Science Foundation of China (61374110, 61333009, 61104078, 61221003) and the Minhang Technology Project of Shanghai (2012MH211).

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

Kai Li¹, Dewei Li¹, Yugeng Xi¹, Debin Yin ²

1. Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China;
2. Shanghai Xinhua Control Technology (Group) Co., Ltd, Shanghai 200241, China

Received:2013-05-06 Revised:2013-10-24 Online:2014-07-28 Published:2014-08-23
Supported by:
Supported by the State Key Laboratory of Synthetical Automation for Process Industries, the National Natural Science Foundation of China (61374110, 61333009, 61104078, 61221003) and the Minhang Technology Project of Shanghai (2012MH211).

摘要/Abstract

摘要： In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by disturbances. The main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control (MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed, which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.

关键词: Gas collector pressure system, Model predictive control, Feedforward

Abstract: In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by disturbances. The main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control (MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed, which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.

Key words: Gas collector pressure system, Model predictive control, Feedforward

Kai Li, Dewei Li, Yugeng Xi, Debin Yin . Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens[J]. , 2014, 22(7): 769-773.

参考文献

[1] X.M. Liu, X.W. Gao,W.Wang, J.S.Wang, Survey on coke oven gas-collector pressure control, 2011 Chinese Control and Decision Conference, 2011, pp. 528-533.
[2] J.S. Yan, Q.Z. Zhang, The microcomputer controlling system of JW-8601 coke oven pressure, Control Inst. Chem. Ind. 19 (1991) 24-27.
[3] H.P. Pan, Coke oven collector pressure control systems based on the uncoupling control, Control Inst. Chem. Ind. 31 (1) (2004) 16-17.
[4] R.K. Gong, Y.B.Wang, B. Zhang, J.Y. Li, X.Y. Shi, Modeling and decoupling for control system of gas collectors pressure, Control Inst. Chem. Ind. 35 (4) (2008) 19-22.
[5] C.H. Yang, M. Wu, D.Y. Shen, G. Deconinck, Hybrid intelligent control of gas collectors of coke ovens, Control. Eng. Pract. 9 (7) (2001) 725-733.
[6] B. Zhou, W.Y. Li, Application of fuzzy theory on gas collector control, Proceeding of the Second International Conference on Machine Learning and Cybernetics, 2003, pp. 2516-2519.
[7] M.Wu, J. Yan, J.H. She,W.H. Cao, Intelligent decoupling control of gas collection process of multiple asymmetric coke ovens, IEEE Trans. Ind. Electron. 56 (2009) 2782-2790.
[8] H.X. Li, E.F. Dou, Y.N. Zhang, Model reference adaptive control using genetic algorithm and neural network for gas collectors of coke ovens, Proceeding of the 2010 IEEE International Conference on Mechatronics and Automation, 2010, pp. 495-500.
[9] Y.G. Xi, Model Predictive Control, National Defense Industry Press, Beijing, China, 1993.
[10] Z.Y. Zou, M. Yu, Z.Z. Wang, X.H. Liu, Y.Q. Guo, F.B. Zhang, N. Cuo, Nonlinear model algorithmic control of a pH neutralization process, Chin. J. Chem. Eng. 21 (4) (2013) 395-400.
[11] Y.C. Hao, Q. Li, W. Tan, D.H. Li, Partially decentralized controller design via model predictive control, Chin. J. Chem. Eng. 20 (6) (2012) 1094-1101.
[12] X.Y. Feng, T. Yu, J.L. Wang, Nonlinear GPC with in-place trained RLS-SVM model for DOC control in a fed-batch bioreactor, Chin. J. Chem. Eng. 20 (5) (2012) 988-994.
[13] W.Y. Zhang, D.X. Huang, Y.D. Wang, J.C. Wang, Adaptive state feedback predictive control and expert control for a delayed coking furnace, Chin. J. Chem. Eng. 16 (4) (2008) 590-598.
[14] B.L. Su, Z.Q. Chen, Z.Z. Yuan, Multivariable decoupling predictive control with input constraints and its application on chemical process, Chin. J. Chem. Eng. 14 (2) (2006) 216-222.
[15] W.H. Shen, X.Q. Chen,M.N. Pons, J.P. Corriou,Model predictive control for wastewater treatment process with feedforward compensation, Chem. Eng. J. 155 (2009) 161-174.
[16] J. Yang, S.H. Li, X.S. Chen, Q. Li, Disturbance rejection of ball mill grinding circuits using DOB and MPC, Powder Technol. 198 (2010) 219-228.
[17] Q.F. Tang, D.W. Li, Y.G. Xi, D.B. Yin, Soft-sensing design based on semiclosed-loop framework, Chin. J. Chem. Eng. 20 (6) (2012) 1213-1218.
[18] P. Zhou, B. Xiang, T.Y. Chai, Improved disturbance observer (DOB) based advanced feedback control for optimal operation of a mineral grinding process, Chin. J. Chem. Eng. 20 (6) (2012) 1206-1212.
[19] J.P. He, H. Li, Chemical Products Recovery Technology in Coking Industry, Metallurgical Industry Press, Beijing, China, 2006.
[20] Y.B. Meng, Q.C. Ji, X.L. Wang, Modeling and simulation for control system of gas collector pressure in coke ovens, J. Syst. Simul. 17 (2005) 2314-2316.

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

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