Feature selection-based machine learning modeling for distributed model predictive control of nonlinear processes

Abstract

In this work, we develop reduced-order machine learning models using feature selection methods for distributed model predictive control (DMPC) of nonlinear processes. Specifically, filter, wrapper, and embedded methods for feature selection are first utilized to select a subset of input features that significantly impacts the prediction of system output. The feature selection methods are then integrated with the development of reduced-order recurrent neural network (RNN) models that capture the system dynamics using only the selected input features. Subsequently, the reduced-order RNN models are incorporated into sequential and iterative DMPCs to stabilize the nonlinear system at the steady-state. Finally, a reactor–reactor–separator process example is used to demonstrate that the DMPC using reduced-order RNN models achieves the desired closed-loop performance with improved computational efficiency.