Artificial neural networks: applications in chemical engineering
Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review...
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Walter de Gruyter GmbH, Berlin/Boston.
2013
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my.utm.493622018-11-09T08:30:37Z http://eprints.utm.my/id/eprint/49362/ Artificial neural networks: applications in chemical engineering Pirdashti, Mohsen Curteanu, Silvia Kamangar, Mehrdad Hashemi Hassim, Mimi Haryani Khatami, Mohammad Amin TP Chemical technology Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review of various ANN applications within the field of chemical engineering (CE). It deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications. A systematic classification scheme is also presented, which uncovers, classifies, and interprets the existing works related to the ANN methodologies and applications within the CE domain. Based on this scheme, 717 scholarly papers from 169 journals are categorized into specific application areas and general (other) applications, including the follo wing topics: petrochemicals, oil and gas industry, biotechnology, cellular industry, environment, health and safety, fuel and energy, mineral industry, nanotechnology, pharmaceutical industry, and polymer industry. It is hoped that this paper will serve as a comprehensive state-of-theart reference for chemical engineers besides highlighting the potential applications of ANN in CE-related problems and consequently enhancing the future ANN research in CE field. Walter de Gruyter GmbH, Berlin/Boston. 2013 Article PeerReviewed Pirdashti, Mohsen and Curteanu, Silvia and Kamangar, Mehrdad Hashemi and Hassim, Mimi Haryani and Khatami, Mohammad Amin (2013) Artificial neural networks: applications in chemical engineering. Reviews In Chemical Engineering, 29 (4). pp. 205-239. ISSN 0167-8299 http://dx.doi.org/10.1515/revce-2013-0013 DOI: 10.1515/revce-2013-0013 |
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TP Chemical technology Pirdashti, Mohsen Curteanu, Silvia Kamangar, Mehrdad Hashemi Hassim, Mimi Haryani Khatami, Mohammad Amin Artificial neural networks: applications in chemical engineering |
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Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review of various ANN applications within the field of chemical engineering (CE). It deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications. A systematic classification scheme is also presented, which uncovers, classifies, and interprets the existing works related to the ANN methodologies and applications within the CE domain. Based on this scheme, 717 scholarly papers from 169 journals are categorized into specific application areas and general (other) applications, including the follo wing topics: petrochemicals, oil and gas industry, biotechnology, cellular industry, environment, health and safety, fuel and energy, mineral industry, nanotechnology, pharmaceutical industry, and polymer industry. It is hoped that this paper will serve as a comprehensive state-of-theart reference for chemical engineers besides highlighting the potential applications of ANN in CE-related problems and consequently enhancing the future ANN research in CE field. |
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Article |
author |
Pirdashti, Mohsen Curteanu, Silvia Kamangar, Mehrdad Hashemi Hassim, Mimi Haryani Khatami, Mohammad Amin |
author_facet |
Pirdashti, Mohsen Curteanu, Silvia Kamangar, Mehrdad Hashemi Hassim, Mimi Haryani Khatami, Mohammad Amin |
author_sort |
Pirdashti, Mohsen |
title |
Artificial neural networks: applications in chemical engineering |
title_short |
Artificial neural networks: applications in chemical engineering |
title_full |
Artificial neural networks: applications in chemical engineering |
title_fullStr |
Artificial neural networks: applications in chemical engineering |
title_full_unstemmed |
Artificial neural networks: applications in chemical engineering |
title_sort |
artificial neural networks: applications in chemical engineering |
publisher |
Walter de Gruyter GmbH, Berlin/Boston. |
publishDate |
2013 |
url |
http://eprints.utm.my/id/eprint/49362/ http://dx.doi.org/10.1515/revce-2013-0013 |
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