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

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Associated organisation

Publication date2007
Host publicationIndustrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE
Place of publicationNew York
PublisherIEEE
Pages644-650
Number of pages7
ISBN (Print)978-1-4244-0783-5
Original languageEnglish

Conference

Conference33rd Annual Conference of the IEEE-Industrial-Electronics-Society
CityTaipei
Period5/11/078/11/07

Publication series

NameIEEE Industrial Electronics Society
PublisherIEEE
ISSN (Print)1553-572X

Conference

Conference33rd Annual Conference of the IEEE-Industrial-Electronics-Society
CityTaipei
Period5/11/078/11/07

Abstract

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.