A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

CIESC Journal ›› 2010, Vol. 18 ›› Issue (1): 66-79.

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

吕文祥^1,2, 朱鹰^1,2, 黄德先^1,2, 江永亨^1,2, 金以慧^1,2

1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China

收稿日期:2009-03-17 修回日期:2009-12-24 出版日期:2010-02-28 发布日期:2010-12-30
通讯作者: HUANG Dexian, E-mail: huangdx@tsinghua.edu.cn
作者简介:
基金资助:
Supported by the National High Technology Research and Development Program of China(2007AA04Z193);the National Natural Science Foundation of China(60974008,60704032)

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

LüWenxiang^1,2, ZHU Ying^1,2, HUANG Dexian^1,2, JIANG Yongheng^1,2, JIN Yihui^1,2

1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China

Received:2009-03-17 Revised:2009-12-24 Online:2010-02-28 Published:2010-12-30
Supported by:
Supported by the National High Technology Research and Development Program of China(2007AA04Z193);the National Natural Science Foundation of China(60974008,60704032)

摘要/Abstract

摘要： For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.

关键词: distillation process control, split ratio, surrogate model optimization, modified differential evolution

Abstract: For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.

Key words: distillation process control, split ratio, surrogate model optimization, modified differential evolution

吕文祥, 朱鹰, 黄德先, 江永亨, 金以慧. A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process[J]. CIESC Journal, 2010, 18(1): 66-79.

LüWenxiang, ZHU Ying, HUANG Dexian, JIANG Yongheng, JIN Yihui. A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process[J]. , 2010, 18(1): 66-79.

参考文献

1 Skogestad, S., “The dos and don’ts of distillation column control”, Chem. Eng. Res. Des., 85 (A1), 12-13 (2007).
2 Nystrom, R.H., Boling, J.M., Ramstedt, J.M., Toivonen, H.T., Haggblom, K.E., “Application of robust and multimodel control methods to an ill-conditioned distillation column”, J. Process Control, 12, 39-53 (2002).
3 Razzaghi, K., Shahraki, F., “Robust control of an ill-conditioned plant using μ-synthesis—A case study for high-purity distillation”, Chem. Eng. Sci., 62, 1543-1547 (2007).
4 Lee J.H., Kesavan P., Morari M., “Control structure selection and robust control system design for a high-purity distillation column”, IEEE Trans. Control Syst. Technol., 5 (4), 402-416 (1997).
5 Sriniwas, G.R., Arkun, Y., Chien, I.L., Ogunnaike, B.A., “Nonlinear identification and control of a high-purity distillation column: A case study”, J. Process Control, 5 (3), 149-162 (1995).
6 Shaw, A.M., Doyle Ⅲ, F.J., “Multivariable nonlinear control applications for a high purity distillation column using a recurrent dynamic neuron model”, J. Process Control, 7 (4), 255-268 (1997).
7 Ramchandran, S., Rhinehartt, R.R., “A very simple structure for neural network control of distillation”, J. Process Control, 5 (2), 115-128 (1995).
8 Richalet, J., Rault, A., Testud, J.L., Papon, J., “Model predictive heuristic control: application to industrial process”, Automatica, 14 (5), 413-428 (1978).
9 Cutler, C.R., Ramarker, B.L., “Dynamic matrix control—A computer control algorithm”, In: Proceedings of 1980 Joint Automatic Control Conference, the American Automatic Control Council, San Francisco, USA (1980).
10 Rouhani, R., Mehra, R.K., “Model algorithmic control (MAC), basic theoretical properties”, Automatica, 18 (4), 401-414 (1982).
11 Qin, S.J., Badgwell, T.A., “A survey of industrial model predictive control technology”, Control Eng. Practice, 11, 733-764 (2003).
12 Olanrewaju, M.J., Al-Arfaj, M.A., “Development and application of linear process model in estimation and control of reactive distillation”, Comput. Chem. Eng., 30, 147-157 (2005).
13 Chatterjee, T., Saraf, D.N., “On-line estimation of product properties for crude distillation units”, J. Process Control, 14, 61-77 (2004).
14 Fortuna, L., Graziani, S., Xibilia, M.G., “Soft sensors for product quality monitoring in debutanizer distillation columns”, Control Eng. Practice, 13, 499-508 (2005).
15 Krishnamurthy, R., Taylor, R., “A nonequilibrium stage model of multicomponent separation processes (I) Model description and method of solution”, AIChE J., 31, 449-456 (1985).
16 Safavi, A.A., Nooraii, A., Romagnoli, J.A., “A hybrid model formulation for a distillation column and the on-line optimisation study”, J. Process Control, 9, 125-134 (1999).
17 Huang, D.X., Lü, W.X., Wang, Y.H., Jin, Y.H., “An automatic control and optimization method of distillation column”, China Pat., 200510086612.8 (2007).
18 Svrcek, W.Y., Mahoney, D.P., Young, B.R., A Real-time Approach to Process Control, John Wiley & Sons, West Sussex, 189 (2000).
19 Shinskey, F.G., Process Control Systems-application, Design and Tuning, 3rd edition, Tsinghua University Press, Beijing, 335 (2004).
20 Wang, L., Lü, W.X., Huang, D.X., “Dynamic flowsheeting simulation and application in the operation analyze of propylene tower”, Comput. Appl. Chem., 23 (1), 83-87 (2006). (in Chinese)
21 Huang, D.X., Wang, J.C, Jin, Y.H., “Stable MIMO constrained predictive control with steady-state objective optimization”, Chin. J. Chem. Eng., 8 (4), 332-338 (2000).
22 Wang, G.G., Dong, Z., Aitchison, P., “Adaptive response surface method—A global optimization scheme for approximation-based design problems”, Eng. Optimiz., 33, 707-733 (2000).
23 Sungwon, K., Ray, T., “A framework for design optimization using surrogates”, Eng. Optimiz., 37 (7), 685-703 (2005).
24 Wang, L., Ji, L.J., Zheng, D.Z., “Simulation optimization based on surrogate model and genetic algorithm”, Control Decis., 19 (6), 626-630 (2004). (in Chinese)
25 Jin,Y., “A comprehensive survey of fitness approximation in evolutionary computation”, Soft Comput., 9 (1), 3-12 (2003).
26 Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd edition, Prentice-Hall, New Jersey (1999).
27 Guo, T.M., Multicomponent Vapour-Liquid Equilibrium and Distillation, Petroleum Industry Press, Beijing (2002).
28 Liu, B., Wang, L., Jin, Y.H., Huang, D.X., Tang, F., “Control and synchronization of chaotic systems by differential evolution algorithm”, Chaos Solitons Fractals, 34, 412-419 (2007).
29 Bhat, T.R., Venkataramani, D., Ravi, V., Murty, C.V.S., “An improved differential evolution method for efficient parameter estimation in biofilter modeling”, Biochem. Eng. J., 28, 167-176 (2006).
30 Lü, W.X., Yang, Q., Huang, D.X., Jin, Y.H., “A dynamic soft-sensing method based on impulses response template and parameter estimation with modified DE optimization”, In: Proceedings of the 17th IFAC World Congress, Elsevier, Seoul, Korea (2008).

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价