Nonlinear Model Algorithmic Control of a pH Neutralization Process

doi:10.1016/S1004-9541(13)60479-6

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (4): 395-400.DOI: 10.1016/S1004-9541(13)60479-6

• 过程系统工程与过程安全 • 上一篇下一篇

Nonlinear Model Algorithmic Control of a pH Neutralization Process

邹志云, 于蒙, 王志甄, 刘兴红, 郭宇晴, 张风波, 郭宁

Research Institute of Pharmaceutical Chemistry, Beijing 102205, China

收稿日期:2012-06-10 修回日期:2012-10-17 出版日期:2013-04-28 发布日期:2013-04-26
通讯作者: ZOU Zhiyun

Nonlinear Model Algorithmic Control of a pH Neutralization Process

ZOU Zhiyun, YU Meng, WANG Zhizhen, LIU Xinghong, GUO Yuqing, ZHANG Fengbo, GUO Ning

Research Institute of Pharmaceutical Chemistry, Beijing 102205, China

Received:2012-06-10 Revised:2012-10-17 Online:2013-04-28 Published:2013-04-26

摘要/Abstract

摘要： Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

关键词: model algorithmic control, nonlinear model predictive control, Hammerstein model, pH neutralization process, control simulation

Abstract: Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

Key words: model algorithmic control, nonlinear model predictive control, Hammerstein model, pH neutralization process, control simulation

邹志云, 于蒙, 王志甄, 刘兴红, 郭宇晴, 张风波, 郭宁. Nonlinear Model Algorithmic Control of a pH Neutralization Process[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 395-400.

ZOU Zhiyun, YU Meng, WANG Zhizhen, LIU Xinghong, GUO Yuqing, ZHANG Fengbo, GUO Ning . Nonlinear Model Algorithmic Control of a pH Neutralization Process[J]. Chin.J.Chem.Eng., 2013, 21(4): 395-400.

参考文献

1 Alvarez, H., Londono, C., “pH neutralization process as a benchmark for testing nonlinear controllers”, Ind. Eng. Chem. Res., 40, 2467-2473 (2001).

2 Nejati, A., Shahrokhi, M., Mehrabani, A., “Comparison between backstepping and input–output linearization techniques for pH process control”, J. Process Control, 22 (1), 263-271 (2012).

3 Altinten, A., “Generalized predictive control applied to a pH neutralization process”, Comput. Chem. Eng., 31 (10), 1199-1204 (2007).

4 Bao, Z.J., Pi, D.Y., Sun, Y.X., “Nonlinear model predictive control based on support vector machine with multi-kernel”, Chin. J. Chem. Eng., 15 (5), 691-697 (2007).

5 Garcia, C.E., Prett, D.M., Morari, M., “Model predictive control: Theory and practice-a survey”, Automatica, 25 (3), 335-348 (1989).

6 Qin, S.J., Badgwell, T.A., “A survey of industrial model predictive control technology”, Control Eng. Practice, 11 (7), 733-764 (2003).

7 Lu, W.X., Zhu, Y., Huang, D.X., Jiang, Y.H., Jin, Y.H., “A new strategy of integrated control and on-line optimization on high-purity distillation process”, Chin. J. Chem. Eng., 18 (1), 66-79 (2010).

8 Zhao, C., Su, H.Y., Gu, Y., Chu, J., “A pragmatic approach for assessing the economic performance of model predictive control systems and its industrial application”, Chin. J. Chem. Eng., 17 (2), 241-250 (2009).

9 Rault, J., Richalet, A., Testud, J.L., Papon, J., “Model predictive heuristic control: application to industrial processes”, Automatica, 14 (5), 413-428 (1978).

10 Mehra, R., Rouhai, R., “Model algorithm control: Review and recent developments”, In: Proceedings of Engineering Foundation Conference on Chemical Process Control II, Sea Island, GA, 287-310 (1982).

11 Zhou, M.F., Wang, S.Q., Jin, X.M., Zhang, Q.L., “Iterative learning model predictive control for a class of continuous/batch processes”, Chin. J. Chem. Eng., 17 (6), 976-982 (2009).

12 Gu, B.F., Gupta, Y.P., “Control of nonlinear processes by using linear model predictive control algorithms”, ISA Transactions, 47 (2), 211-216 (2008).

13 Henson, M.A., “Nonlinear model predictive control: current status and future directions”, Comput. Chem. Eng., 23 (2), 187-202 (1998).

14 Qin, S., Badgwell, T.A., “An overview of nonlinear model predictive control applications”, In Nonlinear Predictive Control, Allgower, F., Zheng, A., eds, Birkhauser, 369-393 (2000).

15 Morari, M., Lee, J.H., “Model predictive control: Past, present and future”, Comput. Chem. Eng., 23 (4-5), 667-682 (1999).

16 Manenti, F., “Considerations on nonlinear model predictive control techniques”, Comput. Chem. Eng., 35 (11), 2491-2509 (2011).

17 Lei, X., Datta, A., “Adaptive model algorithm control”, In: Proceedings of the American Control Conference, Arlington, VA, 4155-4160 (2001).

18 Patikirikorala, T., Wang, L.P., Colman, A., Han, J., “Hammerstein-Wiener nonlinear model based predictive control for relative QoS performance and resource management of software systems”, Control Eng. Practice, 20 (1), 49-61 (2012).

19 Huo, H.B., Zhu, X.J., Hu, W.Q., Tu, H.Y., Li, J., Yang, J., “Nonlinear model predictive control of SOFC based on a Hammerstein model”, Journal of Power Sources, 185 (1), 338-344 (2008).

20 Harnischmacher, G., Marquardt, W., “Nonlinear model predictive control of multivariable processes using block-structured models”, Control Eng. Practice, 15 (10), 1238-1256 (2007).

21 Dolanc, G., Strm?nik, S., “Design of a nonlinear controller based on a piecewise-linear Hammerstein model”, Systems & Control Letters, 57 (4), 332-339 (2008).

22 Fruzzetti, K.P., Palazo?lu, A., McDonald, K.A., “Nolinear model predictive control using Hammerstein models”, J. Process Control, 7 (1), 31-41 (1997).

23 Man, H., Shao, C, “Neural network predictive control of continuous stirred-tank reactor based on Hammerstein-Wiener model”, CIESC Journal, 62 (8), 2275-2280 (2011).

24 ?awryńczuk, M., “On-line set-point optimisation and predictive control using neural Hammerstein models”, Chemical Engineering Journal, 166 (1), 269-287 (2011).

25 Pearson, R.K., “Selecting nonlinear model structures for computer control”, J. Process Control, 13 (1), 1-26 (2003).

26 Bai, E.W., “A blind approach to the Hammerstein-Wiener model identification”, Automatica, 38 (6), 967-979 (2002).

27 Vanbeylen, L., Pintelon, R., Schoukens, J., “Blind maximum likelihood identification of Hammerstein systems”, Automatica, 44 (12), 3139-3146 (2008).

28 Ding, F., Liu, X.P., Liu, G.J., “Identification methods for Hammerstein nonlinear systems”, Digital Signal Processing, 21 (2), 215-238 (2011).

29 Zou, Z.Y., Liu, G.P., Guo, N., “Predictive control of nonlinear Hammerstein systems and application to pH process”, In: Proceedings of European Control Conference 2003, Cambridge, UK, 166 (2003).

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Nonlinear Model Algorithmic Control of a pH Neutralization Process

Nonlinear Model Algorithmic Control of a pH Neutralization Process

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