Implementation of neural network predictive control to a multivariable chemical reactor

Abstract

Implementation of a neural network model-based predictive control scheme to a laboratory-scaled multivariable chemical reactor is described in this paper. Three variables are controlled in the reactor—temperature, pH and dissolved oxygen. The reactor exhibits common features of industrial systems including non-linear dynamics, coupling effects among variables and is without a mathematical model. Multi-input, single-output sub-system models are developed using neural networks and combined to form a parallel process model for simulation and on-line prediction. The process modelling, model-based control simulation, implementation of the on-line control and performance evaluations are investigated and reported in detail in the paper.