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Modeling and optimal control of a batch polymerization reactor using a hybrid stacked recurrent neural network model

Lookup NU author(s): Dr Jie ZhangORCiD


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This paper presents a novel nonlinear hybrid modeling approach aimed at obtaining improvements in model performance and robustness to new data in the optimal control of a batch MMA polymerization reactor. The hybrid model contains a simplified mechanistic model that does not consider the gel effect and stacked recurrent neural networks. Stacked recurrent neural networks are built to characterize the gel effect, which is one of the most difficult parts of polymerization modeling. Sparsely sampled data on polymer quality were interpolated using a cubic spline function to generate data for neural network training. Comparative studies with the use of a single neural network show that stacked networks give superior performance and improved robustness. Optimal reactor temperature control policies are then calculated using the hybrid stacked neural network model. It is shown that the optimal control strategy based on the hybrid stacked neural network model offers much more robust performance than that based on a hybrid single neural network model.

Publication metadata

Author(s): Zhang J; Tian Y; Morris J

Publication type: Article

Publication status: Published

Journal: Industrial and Engineering Chemistry Research

Year: 2001

Volume: 40

Issue: 21

Pages: 4525-4535

ISSN (print): 0888-5885

ISSN (electronic): 1520-5045

Publisher: American Chemical Society


DOI: 10.1021/ie0010565


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