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

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Nonlinear model predictive control of chemical processes with a Wiener identification approach. / Arefi, Mohammad Mehdi; Montazeri, Allahyar; Poshtan, Javad et al.
Industrial Technology, 2006. ICIT 2006. IEEE International Conference on. New York: IEEE, 2006. p. 1735-1740.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Arefi, MM, Montazeri, A, Poshtan, J & Jahed-Motlagh, MR 2006, Nonlinear model predictive control of chemical processes with a Wiener identification approach. in Industrial Technology, 2006. ICIT 2006. IEEE International Conference on. IEEE, New York, pp. 1735-1740, IEEE International Conference on Industrial Technology, Bombay, India, 15/12/06. https://doi.org/10.1109/ICIT.2006.372470

APA

Arefi, M. M., Montazeri, A., Poshtan, J., & Jahed-Motlagh, M. R. (2006). Nonlinear model predictive control of chemical processes with a Wiener identification approach. In Industrial Technology, 2006. ICIT 2006. IEEE International Conference on (pp. 1735-1740). IEEE. https://doi.org/10.1109/ICIT.2006.372470

Vancouver

Arefi MM, Montazeri A, Poshtan J, Jahed-Motlagh MR. Nonlinear model predictive control of chemical processes with a Wiener identification approach. In Industrial Technology, 2006. ICIT 2006. IEEE International Conference on. New York: IEEE. 2006. p. 1735-1740 doi: 10.1109/ICIT.2006.372470

Author

Arefi, Mohammad Mehdi ; Montazeri, Allahyar ; Poshtan, Javad et al. / Nonlinear model predictive control of chemical processes with a Wiener identification approach. Industrial Technology, 2006. ICIT 2006. IEEE International Conference on. New York : IEEE, 2006. pp. 1735-1740

Bibtex

@inproceedings{10f27207a4374ce2a9352f019f514a36,
title = "Nonlinear model predictive control of chemical processes with a Wiener identification approach",
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.",
author = "Arefi, {Mohammad Mehdi} and Allahyar Montazeri and Javad Poshtan and Jahed-Motlagh, {Mohammad Reza}",
year = "2006",
doi = "10.1109/ICIT.2006.372470",
language = "English",
isbn = "978-1-4244-0725-5",
pages = "1735--1740",
booktitle = "Industrial Technology, 2006. ICIT 2006. IEEE International Conference on",
publisher = "IEEE",
note = "IEEE International Conference on Industrial Technology ; Conference date: 15-12-2006 Through 17-12-2006",

}

RIS

TY - GEN

T1 - Nonlinear model predictive control of chemical processes with a Wiener identification approach

AU - Arefi, Mohammad Mehdi

AU - Montazeri, Allahyar

AU - Poshtan, Javad

AU - Jahed-Motlagh, Mohammad Reza

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

U2 - 10.1109/ICIT.2006.372470

DO - 10.1109/ICIT.2006.372470

M3 - Conference contribution/Paper

SN - 978-1-4244-0725-5

SP - 1735

EP - 1740

BT - Industrial Technology, 2006. ICIT 2006. IEEE International Conference on

PB - IEEE

CY - New York

T2 - IEEE International Conference on Industrial Technology

Y2 - 15 December 2006 through 17 December 2006

ER -