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This paper presents a predictive control strategy based on neural networkmodel of the plant is applied to Continuous Stirred Tank Reactor (CSTR). Thissystem is a highly nonlinear process; therefore, a nonlinear predictive method,e.g., neural network predictive control, can be a better match to govern thesystem dynamics. In the paper, the NN model and the way in which it can be usedto predict the behavior of the CSTR process over a certain prediction horizonare described, and some comments about the optimization procedure are made.Predictive control algorithm is applied to control the concentration in acontinuous stirred tank reactor (CSTR), whose parameters are optimallydetermined by solving quadratic performance index using the optimizationalgorithm. An efficient control of the product concentration in cstr can beachieved only through accurate model. Here an attempt is made to alleviate themodeling difficulties using Artificial Intelligent technique such as NeuralNetwork. Simulation results demonstrate the feasibility and effectiveness ofthe NNMPC technique.

Modeling and Control of CSTR using Model based Neural Network Predictive Control