USE OF NEURAL NETS FOR DYNAMIC MODELING AND CONTROL OF CHEMICAL PROCESS SYSTEMS

Neural computing is one of the fastest growing areas of artificial intelligence. Neural nets are inherently parallel and they hold great promise because of their ability to "learn" nonlinear relationships. This paper discusses the use of backpropagation neural nets for dynamic modeling and control of chemical process systems. The backpropagation algorithm and its rationale are reviewed. The algorithm is applied to model the dynamic response of pH in a CSTR. Compared to traditional ARMA modeling, the backpropagation technique is shown to be able to pick up more of the nonlinear characteristics of the CSTR. The use of backpropagation models for control, including learning process inverses, is briefly discussed. © 1990.