Publication:
Early tube leak detection system for steam boiler at KEV power plant

dc.citedby4
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorSingh D.en_US
dc.contributor.authorMaisurah N.en_US
dc.contributor.authorMusa A.B.B.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid57191191317en_US
dc.contributor.authorid56167977700en_US
dc.contributor.authorid55669784800en_US
dc.date.accessioned2023-05-29T06:11:33Z
dc.date.available2023-05-29T06:11:33Z
dc.date.issued2016
dc.descriptionBackpropagation; Coal; Coal fired boilers; Engineering research; Engines; Fault detection; Fossil fuel power plants; Leak detection; Neural networks; Plant shutdowns; Steam power plants; Artificial neural network models; Coal-fired power plant; Feed-forward back propagation networks; Hidden layers; Neural network (nn); Training algorithms; Training function; Working properties; Boilersen_US
dc.description.abstractTube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN) models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN) was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP). The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN) with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants. � 2016 The Authors, published by EDP Sciences.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6
dc.identifier.doi10.1051/matecconf/20167400006
dc.identifier.scopus2-s2.0-84987790060
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84987790060&doi=10.1051%2fmatecconf%2f20167400006&partnerID=40&md5=8d7da08bc7efcd244e8e860583915036
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22669
dc.identifier.volume74
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titleEarly tube leak detection system for steam boiler at KEV power planten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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