Publication:
Hybrid Intelligent Warning System for Boiler tube Leak Trips

Simple item page
dc.citedby1
dc.contributor.authorSingh D.en_US
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorShakir Nasif M.en_US
dc.contributor.authorid57191191317en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid55188481100en_US
dc.date.accessioned2023-05-29T06:37:41Z
dc.date.available2023-05-29T06:37:41Z
dc.date.issued2017
dc.descriptionArtificial intelligence; Boilers; Coal; Fossil fuel power plants; Genetic algorithms; Intelligent systems; Learning systems; Neural networks; Thermoelectric power plants; Artificial intelligent; Coal-fired power plant; Detection and diagnosis; Extreme learning machine; Hybrid intelligent system; Reliable monitoring systems; Thermal power plants; Training algorithms; Monitoringen_US
dc.description.abstractRepeated 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3003
dc.identifier.doi10.1051/matecconf/201713103003
dc.identifier.scopus2-s2.0-85033236736
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236736&doi=10.1051%2fmatecconf%2f201713103003&partnerID=40&md5=5cebc8c54645a36146bb4f74b3acc9da
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23076
dc.identifier.volume131
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titleHybrid Intelligent Warning System for Boiler tube Leak Tripsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS