Modeling and Optimal Control of a Batch Polymerization Reactor Using a Hybrid Stacked Recurrent Neural Network Model

• Published in: Industrial & engineering chemistry research Vol. 40; no. 21; pp. 4525 - 4535
• Main Authors: Tian, Yuan, Zhang, Jie, Morris, Julian
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 17.10.2001
• Subjects: Applied sciences; Exact sciences and technology; Industrial polymers. Preparations; Polymer industry, paints, wood; Technology of polymers; Thermoplastics; Kinetic model; Free radical polymerization; Recurrent neural networks; Batchwise; Optimal control; Theoretical study; Methyl methacrylate polymer; Hybrid model; Modeling; Solution polymerization
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/ie0010565

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.