Adaptive neural network model based nonlinear predictive control of a fluid catalytic cracking unit

Z. Nagy, S. Agachi, L. Bodizs

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Neural Networks are used for a wide variety of chemical applications because of their ability to learn system features. This paper presents the use of artificial neural networks (ANN) for dynamic modeling and nonlinear model predictive control of a fluid catalytic unit (FCCU). An off-line trained ANN model based predictive control structure (NNMPC) and on adaptive neural network model based predictive control (ANNMPC) scheme were tested. Both control structures give a superior control performance compared to the classical proportional-integral (PI) controllers. To improve the convergence of the optimization process in both the off-line or on-line training of the ANN model and in the on-line control problem the use of genetic algorithm (GA) in combination with the classical optimization algorithms was proposed.

Original languageEnglish
Pages (from-to)235-240
Number of pages6
JournalComputer Aided Chemical Engineering
Volume8
Issue numberC
DOIs
Publication statusPublished - 2000

Fingerprint

Fluid catalytic cracking
Neural networks
Model predictive control
Genetic algorithms
Controllers
Fluids

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

@article{d6f8d813d0674301be7b15ba1690d38f,
title = "Adaptive neural network model based nonlinear predictive control of a fluid catalytic cracking unit",
abstract = "Neural Networks are used for a wide variety of chemical applications because of their ability to learn system features. This paper presents the use of artificial neural networks (ANN) for dynamic modeling and nonlinear model predictive control of a fluid catalytic unit (FCCU). An off-line trained ANN model based predictive control structure (NNMPC) and on adaptive neural network model based predictive control (ANNMPC) scheme were tested. Both control structures give a superior control performance compared to the classical proportional-integral (PI) controllers. To improve the convergence of the optimization process in both the off-line or on-line training of the ANN model and in the on-line control problem the use of genetic algorithm (GA) in combination with the classical optimization algorithms was proposed.",
author = "Z. Nagy and S. Agachi and L. Bodizs",
year = "2000",
doi = "10.1016/S1570-7946(00)80041-3",
language = "English",
volume = "8",
pages = "235--240",
journal = "Computer Aided Chemical Engineering",
issn = "1570-7946",
publisher = "Elsevier",
number = "C",

}

Adaptive neural network model based nonlinear predictive control of a fluid catalytic cracking unit. / Nagy, Z.; Agachi, S.; Bodizs, L.

In: Computer Aided Chemical Engineering, Vol. 8, No. C, 2000, p. 235-240.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive neural network model based nonlinear predictive control of a fluid catalytic cracking unit

AU - Nagy, Z.

AU - Agachi, S.

AU - Bodizs, L.

PY - 2000

Y1 - 2000

N2 - Neural Networks are used for a wide variety of chemical applications because of their ability to learn system features. This paper presents the use of artificial neural networks (ANN) for dynamic modeling and nonlinear model predictive control of a fluid catalytic unit (FCCU). An off-line trained ANN model based predictive control structure (NNMPC) and on adaptive neural network model based predictive control (ANNMPC) scheme were tested. Both control structures give a superior control performance compared to the classical proportional-integral (PI) controllers. To improve the convergence of the optimization process in both the off-line or on-line training of the ANN model and in the on-line control problem the use of genetic algorithm (GA) in combination with the classical optimization algorithms was proposed.

AB - Neural Networks are used for a wide variety of chemical applications because of their ability to learn system features. This paper presents the use of artificial neural networks (ANN) for dynamic modeling and nonlinear model predictive control of a fluid catalytic unit (FCCU). An off-line trained ANN model based predictive control structure (NNMPC) and on adaptive neural network model based predictive control (ANNMPC) scheme were tested. Both control structures give a superior control performance compared to the classical proportional-integral (PI) controllers. To improve the convergence of the optimization process in both the off-line or on-line training of the ANN model and in the on-line control problem the use of genetic algorithm (GA) in combination with the classical optimization algorithms was proposed.

UR - http://www.scopus.com/inward/record.url?scp=77957235994&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77957235994&partnerID=8YFLogxK

U2 - 10.1016/S1570-7946(00)80041-3

DO - 10.1016/S1570-7946(00)80041-3

M3 - Article

AN - SCOPUS:77957235994

VL - 8

SP - 235

EP - 240

JO - Computer Aided Chemical Engineering

JF - Computer Aided Chemical Engineering

SN - 1570-7946

IS - C

ER -