Use of Neural Nets For Dynamic Modeling and Control of Chemical Process Systems

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 succesfully to model the dynamic response of pH in a CSTR. The use of backpropagation models for control is briefly discussed.