GA based optimal control of a batch reactor with SVM tuned GMC

This paper presents the application of machine learning strategy namely SVM and GA for process modeling and optimization of Batch reactor. Batch reactor is an essential unit operation in almost all batch-processing industries like chemical and pharmaceuticals etc. The dynamic optimization of batch reactor has received major attention always, so that an increase in the yield may be obtained by using the optimal temperature profiles. The optimal reactor temperature profiles are obtained by solving optimal control problems off-line. In this approach, the temperature profile of the batch reactor is optimized using Genetic Algorithm (GA) with a view to maximize the desired product and minimize the waste product as a multi-objective function. Here Generic Model Control is implemented by using SVM Estimator and it includes the nonlinear model of a process to determine the control action. SVM model will act as a estimator will predict the current value of the heat release makes the control performance to be more robust. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc) is not required. The robustness performance of proposed optimal controller has been experienced.