Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor

Some chemical plants such as plug-flow tubular reactors have highly nonlinear behavior. Such processes demand a powerful identification method such as a neural-networks-based Wiener model. In this paper, a plug-flow reactor is 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-based Wiener identification method, and two linear and nonlinear model predictive controllers are applied with the ability of rejecting slowly varying unmeasured disturbances. The results are also compared with a common PI controller for temperature control of tubular reactor. Simulation results show that the obtained Wiener model has a good capability to predict the step response of the process. Parameters of both linear and nonlinear model predictive controllers are tuned and the best-obtained results are compared. For this purpose, different operating points are selected to have a wide range of operation for the nonlinear process. It is shown that the nonlinear controller has the fastest damped response in comparison with the other two controllers.