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Pure intelligent monitoring system for steam economizer trips

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dc.contributor.authorBasim Ismail F.en_US
dc.contributor.authorHamzah Abed K.en_US
dc.contributor.authorSingh D.en_US
dc.contributor.authorShakir Nasif M.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid57196436991en_US
dc.contributor.authorid57191191317en_US
dc.contributor.authorid55188481100en_US
dc.date.accessioned2023-05-29T06:37:42Z
dc.date.available2023-05-29T06:37:42Z
dc.date.issued2017
dc.descriptionEconomizers; Failure (mechanical); Fault detection; Knowledge acquisition; Learning algorithms; Learning systems; Neural networks; Plant shutdowns; Steam; Thermoelectric power plants; Extreme learning machine; Fault detection and diagnosis systems; Intelligent modeling; Intelligent monitoring systems; Network methodologies; Operational conditions; Operational variables; Thermal power plants; Steam power plantsen_US
dc.description.abstractSteam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well. � The authors, published by EDP Sciences, 2017.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4008
dc.identifier.doi10.1051/matecconf/201713104008
dc.identifier.scopus2-s2.0-85033229462
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85033229462&doi=10.1051%2fmatecconf%2f201713104008&partnerID=40&md5=70ddacc6d016e7a6029f50bd303584e8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23081
dc.identifier.volume131
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titlePure intelligent monitoring system for steam economizer tripsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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