Hybrid neural network-prior knowledge model in temperature control of a semi-batch polymerization process

Nonlinear process control is a challenging research topic at present. In recent years, neural network and hybrid neural networks have been much studied especially for modeling of nonlinear system. It has however been applied mainly as an estimator in parts of various control systems and the idea of utilizing it directly as a neural-controller has not been studied. Hence the contribution of this work is to use an inverse neural network in hybrid with a first principle model for the direct control of a nonlinear semi-batch polymerization process. These hybrid models were utilized in the direct inverse control strategy to track the set point of the temperature of the polymerization reactor under nominal condition and with various disturbances. For comparison purposes, the standard neural network and proportional-integral-derivative controller were also implemented in these control strategies. Adaptation mechanisms to improve the results have also been carried out to test the capability of these hybrid methods in control. The simulation results show the advantages and robustness of utilizing the neural network in this hybrid strategy especially when an adaptive algorithm is implemented. (C) 2003 Elsevier B.V. All rights reserved.