An efficient latent variable optimization approach with stochastic constraints for complex industrial process

doi:10.1016/j.cjche.2015.05.009

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (10): 1670-1678.DOI: 10.1016/j.cjche.2015.05.009

• 过程系统工程与过程安全 • 上一篇下一篇

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

Zhengshun Fei¹, Kangling Liu², Bin Hu³, Jun Liang²

1 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
2 State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
3 Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

收稿日期:2015-01-15 修回日期:2015-04-17 出版日期:2015-10-28 发布日期:2015-11-27
通讯作者: Zhengshun Fei
基金资助:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), the Natural Science Foundation of Zhejiang Province (LQ15F030006), and the Educational Commission Research Program of Zhejiang Province (Y201431412).

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

Zhengshun Fei¹, Kangling Liu², Bin Hu³, Jun Liang²

1 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
2 State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
3 Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

Received:2015-01-15 Revised:2015-04-17 Online:2015-10-28 Published:2015-11-27
Supported by:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), the Natural Science Foundation of Zhejiang Province (LQ15F030006), and the Educational Commission Research Program of Zhejiang Province (Y201431412).

摘要/Abstract

摘要： For complex chemical processes, process optimization is usually performed on causalmodels fromfirst principle models. When the mechanism models cannot be obtained easily, restricted model built by process data is used for dynamic process optimization. A new strategy is proposed for complex process optimization, in which latent variables are used as decision variables and statistics is used to describe constraints. As the constraint condition will be more complex by projecting the original variable to latent space, Hotelling T² statistics is introduced for constraint formulation in latent space. In this way, the constraint is simplified when the optimization is solved in low-dimensional space of latent variable. The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

关键词: Data-driven model, Optimization, Partial least square, Polymerization

Abstract: For complex chemical processes, process optimization is usually performed on causalmodels fromfirst principle models. When the mechanism models cannot be obtained easily, restricted model built by process data is used for dynamic process optimization. A new strategy is proposed for complex process optimization, in which latent variables are used as decision variables and statistics is used to describe constraints. As the constraint condition will be more complex by projecting the original variable to latent space, Hotelling T² statistics is introduced for constraint formulation in latent space. In this way, the constraint is simplified when the optimization is solved in low-dimensional space of latent variable. The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

Key words: Data-driven model, Optimization, Partial least square, Polymerization

Zhengshun Fei, Kangling Liu, Bin Hu, Jun Liang. An efficient latent variable optimization approach with stochastic constraints for complex industrial process[J]. Chinese Journal of Chemical Engineering, 2015, 23(10): 1670-1678.

Zhengshun Fei, Kangling Liu, Bin Hu, Jun Liang. An efficient latent variable optimization approach with stochastic constraints for complex industrial process[J]. Chin.J.Chem.Eng., 2015, 23(10): 1670-1678.

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An efficient latent variable optimization approach with stochastic constraints for complex industrial process

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

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