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Hybrid Intelligent Warning System for Boiler tube Leak Trips

Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak...

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Main Authors: Singh, D., Ismail, F.B., Shakir Nasif, M.
Format: Article
Published: EDP Sciences 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236736&doi=10.1051%2fmatecconf%2f201713103003&partnerID=40&md5=5cebc8c54645a36146bb4f74b3acc9da
http://eprints.utp.edu.my/19971/
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spelling my.utp.eprints.199712018-04-22T14:29:11Z Hybrid Intelligent Warning System for Boiler tube Leak Trips Singh, D. Ismail, F.B. Shakir Nasif, M. Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers. © The authors, published by EDP Sciences, 2017. EDP Sciences 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236736&doi=10.1051%2fmatecconf%2f201713103003&partnerID=40&md5=5cebc8c54645a36146bb4f74b3acc9da Singh, D. and Ismail, F.B. and Shakir Nasif, M. (2017) Hybrid Intelligent Warning System for Boiler tube Leak Trips. MATEC Web of Conferences, 131 . http://eprints.utp.edu.my/19971/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers. © The authors, published by EDP Sciences, 2017.
format Article
author Singh, D.
Ismail, F.B.
Shakir Nasif, M.
spellingShingle Singh, D.
Ismail, F.B.
Shakir Nasif, M.
Hybrid Intelligent Warning System for Boiler tube Leak Trips
author_facet Singh, D.
Ismail, F.B.
Shakir Nasif, M.
author_sort Singh, D.
title Hybrid Intelligent Warning System for Boiler tube Leak Trips
title_short Hybrid Intelligent Warning System for Boiler tube Leak Trips
title_full Hybrid Intelligent Warning System for Boiler tube Leak Trips
title_fullStr Hybrid Intelligent Warning System for Boiler tube Leak Trips
title_full_unstemmed Hybrid Intelligent Warning System for Boiler tube Leak Trips
title_sort hybrid intelligent warning system for boiler tube leak trips
publisher EDP Sciences
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236736&doi=10.1051%2fmatecconf%2f201713103003&partnerID=40&md5=5cebc8c54645a36146bb4f74b3acc9da
http://eprints.utp.edu.my/19971/
_version_ 1738656145382309888
score 13.211869

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