12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Nonlinear model predictive control of chemical ...
View graph of relations

« Back

Nonlinear model predictive control of chemical processes with a Wiener identification approach

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published

Associated organisation

Publication date2006
Host publicationIndustrial Technology, 2006. ICIT 2006. IEEE International Conference on
Place of publicationNew York
PublisherIEEE
Pages1735-1740
Number of pages6
ISBN (Print)978-1-4244-0725-5
Original languageEnglish

Conference

ConferenceIEEE International Conference on Industrial Technology
CountryIndia
CityBombay
Period15/12/0617/12/06

Conference

ConferenceIEEE International Conference on Industrial Technology
CountryIndia
CityBombay
Period15/12/0617/12/06

Abstract

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.