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Neural network inverse model-based controller for the control of a steel pickling process

The present work investigates the use of neural network direct inverse model-based control strategy (NNDIC) to control a steel pickling process. The process is challenging due to the fact that the pH of effluent streams must be regulated accurately to protect aquatic and human welfare, and to comply...

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Main Authors: Daosud, Wachira, Thitiyasook, Piyanuch, Arpornwichanop, Amornchai, Kittisupakorn, Paisan, Hussain, Mohd Azlan
Format: Article
Published: Elsevier 2005
Subjects:
TA Engineering (General). Civil engineering (General)
TP Chemical technology
Online Access:http://eprints.um.edu.my/7058/
https://doi.org/10.1016/j.compchemeng.2005.06.007
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spelling my.um.eprints.70582021-02-10T02:58:59Z http://eprints.um.edu.my/7058/ Neural network inverse model-based controller for the control of a steel pickling process Daosud, Wachira Thitiyasook, Piyanuch Arpornwichanop, Amornchai Kittisupakorn, Paisan Hussain, Mohd Azlan TA Engineering (General). Civil engineering (General) TP Chemical technology The present work investigates the use of neural network direct inverse model-based control strategy (NNDIC) to control a steel pickling process. The process is challenging due to the fact that the pH of effluent streams must be regulated accurately to protect aquatic and human welfare, and to comply with limits imposed by legislation. At the same time, the concentration of acid solution in the pickling step needs to be maintained at the optimum value in order to obtain the maximum reaction rate. Various changes in the open-loop dynamics are performed before implementation of the inverse neural network modeling technique. The optimal neural network architectures are determined by the mean squared error (MSE) minimization technique. The robustness of the proposed inverse model neural network control strategy is investigated with respect to changes in disturbances, model mismatch and noise effects. Simulation results show the superiority of the NNDIC controller in the cases involving disturbance, model mismatch and noise while the conventional controller gives better results in the nominal case. Elsevier 2005 Article PeerReviewed Daosud, Wachira and Thitiyasook, Piyanuch and Arpornwichanop, Amornchai and Kittisupakorn, Paisan and Hussain, Mohd Azlan (2005) Neural network inverse model-based controller for the control of a steel pickling process. Computers & Chemical Engineering, 29 (10). pp. 2110-2119. ISSN 0098-1354 https://doi.org/10.1016/j.compchemeng.2005.06.007 doi:10.1016/j.compchemeng.2005.06.007
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
TP Chemical technology
spellingShingle TA Engineering (General). Civil engineering (General)
TP Chemical technology
Daosud, Wachira
Thitiyasook, Piyanuch
Arpornwichanop, Amornchai
Kittisupakorn, Paisan
Hussain, Mohd Azlan
Neural network inverse model-based controller for the control of a steel pickling process
description The present work investigates the use of neural network direct inverse model-based control strategy (NNDIC) to control a steel pickling process. The process is challenging due to the fact that the pH of effluent streams must be regulated accurately to protect aquatic and human welfare, and to comply with limits imposed by legislation. At the same time, the concentration of acid solution in the pickling step needs to be maintained at the optimum value in order to obtain the maximum reaction rate. Various changes in the open-loop dynamics are performed before implementation of the inverse neural network modeling technique. The optimal neural network architectures are determined by the mean squared error (MSE) minimization technique. The robustness of the proposed inverse model neural network control strategy is investigated with respect to changes in disturbances, model mismatch and noise effects. Simulation results show the superiority of the NNDIC controller in the cases involving disturbance, model mismatch and noise while the conventional controller gives better results in the nominal case.
format Article
author Daosud, Wachira
Thitiyasook, Piyanuch
Arpornwichanop, Amornchai
Kittisupakorn, Paisan
Hussain, Mohd Azlan
author_facet Daosud, Wachira
Thitiyasook, Piyanuch
Arpornwichanop, Amornchai
Kittisupakorn, Paisan
Hussain, Mohd Azlan
author_sort Daosud, Wachira
title Neural network inverse model-based controller for the control of a steel pickling process
title_short Neural network inverse model-based controller for the control of a steel pickling process
title_full Neural network inverse model-based controller for the control of a steel pickling process
title_fullStr Neural network inverse model-based controller for the control of a steel pickling process
title_full_unstemmed Neural network inverse model-based controller for the control of a steel pickling process
title_sort neural network inverse model-based controller for the control of a steel pickling process
publisher Elsevier
publishDate 2005
url http://eprints.um.edu.my/7058/
https://doi.org/10.1016/j.compchemeng.2005.06.007
_version_ 1691733421368279040
score 13.211869

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