Abstract
For large-scale chemical process, which consists of lots of production units, all units have their respective optimization objects which are often conflicting with each other for a series of constraints on material and energy balance. In this paper, the total solution with two layers structure strategy made up of multi-units unified optimization and predictive control of each unit is realized. For the global optimization has high dimension, serious nonlinearity and uncertainty, the optimization algorithm based on differential evolution (DE) is performed, while a hybrid DE approach combining hypothesis test (HT) to compare the optimization objects under uncertainty is proposed. The simulation results of an application example to a 20Mt/a gas separation process show that the proposed total solution with two layers structure strategy is successful and multi-units unified optimization method based on HTDE is effective and robust for solving the optimization problem under uncertainty.
This research is supported by National Science Foundation of China (Grant No. 60574072) as well as the National high tech. project of China(863/CIMS 2006AA04Z168).
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References
Vargas-Villamil, F.D., Rivera, D.E.: A Model Predictive Control Approach for Real-time Optimization of Reentrant Manufacturing Lines. Computers in Industry 45(1), 45–57 (2001)
Huang, D.X., Wang, J.C., Jin, Y.H.: Stable MIMO Constrained Predictive Control with Steady-state Objective Optimization. Chinese Journal of Chemical Engineering 8(4), 332–338 (2000)
Huang, D.X.: Studies and Applications on the Methodology of Multi-variables Predictive Control. Beijing: Dep. Of Automation, Tsinghua University (in Chinese) (1999)
Qin, V., Badgwell, T.A.: A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice 11, 733–764 (2003)
Pham, Q.T.: Dynamic Optimization of Chemical Engineering Processes by an Evolutionary Method. Computers and Chemical Engineering 22, 1089–1097 (1998)
Storn, R., Price, K.: Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J Global Optim II, 341–359 (1997)
Ilonen, J., Kamarainen, J.K., Lampinen, J.: Differential Evolution Training Algorithm for Feed-forward Neural Networks. Neural Process Lett 17(1), 93–105 (2003)
Chang, F.P., Hwang, C.: Design of Digital PID Controllers for Continuous-time Plants with Integral Performance Criteria. J Chin Inst Chem Eng 35(6), 683–696 (2004)
Chang, Y.P., Wu, C.J.: Optimal Multiobjective Planning of Large-scale Passive Harmonic Filters using Hybrid Differential Evolution Method Considering Parameter and Loading Uncertainty. IEEE T Power Deliver 20(1), 408–416 (2005)
Zhang, Y.Y., Shao, Z.J., Zhong, W.T., et al.: Centralized Optimization and Decentralized Optimization of Distilation Columns in Series. Automation in Petro-Chemical Industry 3, 19–23 (in Chinese) (1999)
Price, K., Storn, R.: Differential Evolution (DE) for Continuous Function Optimization (2007), http://www.icsi.berkeley.edu/%7Estorn/code.html
Zhang, Y., Monder, D., Forbes, J.F.: Real-time Optimization under Parametric Uncertainty: A Probability Constrained Approach. J. Process Contr. 12, 373–389 (2002)
Huang, D.X., Gu, J., Wang, Y.H.: On-Line Application Platform Based on Flowsheeting Simulation. Process Control Science Technology and Applications, 823–827 (in Chinese) (2002)
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Lv, W., Qian, B., Huang, D., Jin, Y. (2007). Multi-units Unified Process Optimization Under Uncertainty Based on Differential Evolution with Hypothesis Test. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_124
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DOI: https://doi.org/10.1007/978-3-540-74205-0_124
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