Economic MPC of Nonlinear Processes via Recurrent Neural Networks Using Structural Process Knowledge

Abstract

This work proposes three methods that incorporate a priori process knowledge into recurrent neural network (RNN) modeling of nonlinear processes to improve prediction accuracy and provide insights on the structure of neural network models. Specifically, we discuss a hybrid modeling method that integrates first-principles models and RNNs together, 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 introduces weight constraints in the optimization problem of RNN model training, respectively. The proposed RNN modeling methods are applied in the context of economic model predictive control of a chemical process example to demonstrate their improved approximation performance compared to a fully-connected RNN model that is developed as a black box model.