Review of the applications of neural networks in chemical process control—simulation and online implementation
As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identi®cation. However, only in recent years, with the upsurge in the research on nonlinear con...
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Artificial Intelligence in Engineering
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Online Access: | http://ac.els-cdn.com/S0954181098000119/1-s2.0-S0954181098000119-main.pdf?_tid=ab92acbc-860c-11e2-a394-00000aab0f26&acdnat=1362540155_2713963a2211db3d0938bdfb137fbde7 http://ac.els-cdn.com/S0954181098000119/1-s2.0-S0954181098000119-main.pdf?_tid=ab92acbc-860c-11e2-a394-00000aab0f26&acdnat=1362540155_2713963a2211db3d0938bdfb137fbde7 http://eprints.um.edu.my/7097/1/Azlan_Hussain%2D1999%2DReview_of_the_applic.pdf |
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um-70972013-07-11T01:03:33Z Review of the applications of neural networks in chemical process control—simulation and online implementation Azlan Hussain, Mohamed TA Engineering (General). Civil engineering (General) TP Chemical technology As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identi®cation. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time. q1998 Elsevier Science Ltd. All rights reserved. Artificial Intelligence in Engineering 1999 Article PeerReviewed application/pdf http://eprints.um.edu.my/7097/1/Azlan_Hussain%2D1999%2DReview_of_the_applic.pdf http://ac.els-cdn.com/S0954181098000119/1-s2.0-S0954181098000119-main.pdf?_tid=ab92acbc-860c-11e2-a394-00000aab0f26&acdnat=1362540155_2713963a2211db3d0938bdfb137fbde7 Azlan Hussain, Mohamed (1999) Review of the applications of neural networks in chemical process control—simulation and online implementation. Artificial Intelligence in Engineering <http://eprints.um.edu.my/view/publication/Artificial_Intelligence_in_Engineering.html>, 13 (1). pp. 55-68. ISSN 0954-1810 http://eprints.um.edu.my/7097/ |
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TA Engineering (General). Civil engineering (General) TP Chemical technology |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Azlan Hussain, Mohamed Review of the applications of neural networks in chemical process control—simulation and online implementation |
description |
As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identi®cation. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time. q1998 Elsevier Science Ltd. All rights reserved. |
format |
Article |
author |
Azlan Hussain, Mohamed |
author_facet |
Azlan Hussain, Mohamed |
author_sort |
Azlan Hussain, Mohamed |
title |
Review of the applications of neural networks in chemical process control—simulation and online implementation |
title_short |
Review of the applications of neural networks in chemical process control—simulation and online implementation |
title_full |
Review of the applications of neural networks in chemical process control—simulation and online implementation |
title_fullStr |
Review of the applications of neural networks in chemical process control—simulation and online implementation |
title_full_unstemmed |
Review of the applications of neural networks in chemical process control—simulation and online implementation |
title_sort |
review of the applications of neural networks in chemical process control—simulation and online implementation |
publisher |
Artificial Intelligence in Engineering |
publishDate |
1999 |
url |
http://ac.els-cdn.com/S0954181098000119/1-s2.0-S0954181098000119-main.pdf?_tid=ab92acbc-860c-11e2-a394-00000aab0f26&acdnat=1362540155_2713963a2211db3d0938bdfb137fbde7 http://ac.els-cdn.com/S0954181098000119/1-s2.0-S0954181098000119-main.pdf?_tid=ab92acbc-860c-11e2-a394-00000aab0f26&acdnat=1362540155_2713963a2211db3d0938bdfb137fbde7 http://eprints.um.edu.my/7097/1/Azlan_Hussain%2D1999%2DReview_of_the_applic.pdf |
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2018-09-06T05:24:04Z |
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2018-09-06T05:24:04Z |
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