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On-line condition monitoring system for high level trip water in steam Boiler's Drum

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dc.citedby1
dc.contributor.authorAlnaimi F.B.I.en_US
dc.contributor.authorA Ali M.en_US
dc.contributor.authorAl-Kayiem H.H.en_US
dc.contributor.authorMohamed Sahari K.S.B.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid56297844400en_US
dc.contributor.authorid6507544662en_US
dc.contributor.authorid57218170038en_US
dc.date.accessioned2023-05-16T02:47:06Z
dc.date.available2023-05-16T02:47:06Z
dc.date.issued2014
dc.description.abstractThis paper presents a monitoring technique using Artificial Neural Networks (ANN) with four different training algorithms for high level water in steam boiler's drum. Four Back-Propagations neural networks multidimensional minimization algorithms have been utilized. Real time data were recorded from power plant located in Malaysia. The developed relevant variables were selected based on a combination of theory, experience and execution phases of the model. The Root Mean Square (RMS) Error has been used to compare the results of one and two hidden layer (1HL), (2HL) ANN structures. © 2014 Owned by the authors, published by EDP Sciences.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3011
dc.identifier.doi10.1051/matecconf/20141303011
dc.identifier.scopus2-s2.0-84904988619
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84904988619&doi=10.1051%2fmatecconf%2f20141303011&partnerID=40&md5=fb950bebff63532b2cbef68f09a2f279
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22068
dc.identifier.volume13
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titleOn-line condition monitoring system for high level trip water in steam Boiler's Drumen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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