Nonlinear Model Predictive Control of Chemical Processes with a Wiener Identification Approach

Some chemical plants such as pH neutralization process have highly nonlinear behavior. Such processes demand a powerful Wiener identification approach based on neural networks for identification of the nonlinear part. In this paper, the pH neutralization process is identified with NN-based Wiener identification method and two linear and nonlinear model predictive controllers with the ability of rejecting slowly varying unmeasured disturbances are applied. Simulation results show that the obtained Wiener model has 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. Simulation results show that the nonlinear controller has better performance without any overshoot in comparison with linear MPC and also less steady-state error in tracking the set-points.