Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor

In this paper, identification and nonlinear model predictive control of highly nonlinear plug-flow tubular reactor based on Wiener model is studied. This process simulated in a rather realistic environment by HYSYS, and the obtained data is in connection with MATLAB for identification and control purpose. The process is identified with NN-Wiener identification method, and two linear and nonlinear model predictive controllers are applied with the ability of rejecting slowly varying unmeasured disturbance. Since the identification problem must be solved with a nonlinear optimization method, to attain the best possible model for prediction genetic algorithm is used. The Simulation results show that the obtained Wiener model has a good capability to predict the step response of the process. The results for control are also compared with a common PI controller for temperature control of tubular reactor. It is shown that the nonlinear controller has the fastest damped response in comparison with the other two controllers.