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

Arefi, Mohammad M. and Montazeri, A. and Poshtan, J. and Jahed-Motlagh, M. R. (2008) Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor. Chemical Engineering Journal, 138 (1-3). pp. 274-282. ISSN 1385-8947

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Chemical Engineering Journal
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2209
Subjects:
ID Code:
65622
Deposited By:
Deposited On:
12 Jul 2013 12:19
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Oct 2020 02:35