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An efficient latent variable optimization approach with stochastic constraints for complex industrial process 被引量:1

复杂工业过程中一种带统计形式约束的隐变量优化方法(英文)
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摘要 For complex chemical processes,process optimization is usually performed on causal models from first 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 T^2 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. For complex chemical processes,process optimization is usually performed on causal models from first 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 T^2 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.
出处 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第10期1670-1678,共9页 中国化学工程学报(英文版)
基金 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) the Educational Commission Research Program of Zhejiang Province(Y201431412)
关键词 Data-driven model OPTIMIZATION Partial least square POLYMERIZATION 变量优化 复杂工业过程 随机约束 因果模型 统计方法 化学过程 第一原理 流程优化
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  • 1Z. Ge, T. Chen, Z. Song, Quality prediction for polypropylene production process based on CLGPR model, Control Eng. Pract. 19 (2011) 423-432.
  • 2P. Kadlec, B. Gabrys, S. Strandt, Data-driven soft sensors in the process industry, Comput. Chem. Eng. 33 (2009) 795-814.
  • 3S. Wold, A. Ruhe, H. Wold, W.J. Dunn, The collinearity problem in linear-regression - the partial least-squares (PLS) approach to generalized inverses, SIAMJ. Sci. Smt. Comput. 5 (1984) 735-743.
  • 4E. Tomba, P. Facco, F. Bezzo, S. Garcia-Mu~oz, Exploiting historical databases to de- sign the target quality profile for a new product, Ind. Eng. Chem. Res. 52 (2013) 8260-8271.
  • 5E. Tomba, P. Facco, F. Bezzo, M. Barolo, Latent variable modeling to assist the imple- mentation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: a review, lnt.J. Pharm. 457 (2013) 283-297.
  • 6F. Yacoub, J.F. MacGregor, Product optimization and control in the latent variable space of nonlinear PLS models, Chemom. IntelL Lab. Syst. 70 (2004) 63-74.
  • 7C.M. Jaeckle, J.F. MacGregor, Product design through multivariate statistical analysis of process data, AIChEJ. 44 (1998) 1105-1118.
  • 8S. Garda-Munoz, T. Kourti, ],F. MacGregor, F. Apruzzese, M. Champagne, Optimiza- tion of batch operating policies. Part I. Handling multiple solutions, Ind. Eng. Chem. Res. 45 (2006) 7856-7866.
  • 9E. Tomba, M. Barolo, S. Garcia-Munoz, General framework for latent variable model inversion for the design and manufacturing of new products, Ind. Eng. Chem. Res. 51 (2012) 12886-12900.
  • 10S. Garcia-Munoz, J.F. MacGregor, D. Nengi, B.E. Latshaw, S. Mehta, Optimization of batch operating policies. Part IL Incorporating process constraints and industrial ap- plications, Ind. Eng. Chem. Res. 47 (2008) 4202-4208.

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