Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes. (May 2020)

• Record Type: Journal Article
• Authors: Wu, Zhe; Rincon, David; Christofides, Panagiotis D.
• Is Part Of: Journal of process control. Volume 89(2020)
• Page Start: 74
• Page End: 84
• Publication Date: 2020-05
• Subjects: Recurrent neural networks -- Model predictive control -- Structural process knowledge -- Nonlinear systems -- Chemical processes; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.03.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Recurrent neural network (RNN) modeling using structural process knowledge. Economic model predictive control (EMPC) using process-aware RNN models. Process operation and production optimization via EMPC. Evaluation of approach using a chemical process network example. Abstract: In this work, physics-based recurrent neural network (RNN) modeling approaches are proposed for a general class of nonlinear dynamic process systems to improve prediction accuracy by incorporating a priori process knowledge. Specifically, a hybrid modeling method is first introduced to integrate first-principles models and RNN models. Subsequently, a partially-connected RNN modeling method that designs the RNN structure based on a priori structural process knowledge, and a weight-constrained RNN modeling method that employs weight constraints in the optimization problem of the RNN training process are developed. The proposed physics-based RNN models are utilized in model predictive controllers and applied to a chemical process network example to demonstrate their improved approximation performance compared to the fully-connected RNN model that is developed as a black box model.