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 Wiener identification approach based on neural networks for identification of the nonlinear part. In this paper, a plug-flow reactor is simulated in a more realistic environment by HYSYS and the obtained data are connected 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 with the ability of rejecting slowly varying unmeasured disturbances are applied. The results are also compared with a common PI controller for control of the temperature 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.

کلیدواژه ها:

Wiener Neural Identification ، Nonlinear Model Predictive Control ، Test Signal ، Tubular Reactor ، HYSYS Simulator