Publication:
Adoption of intelligent computational techniques for steam boilers tube leak trip

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dc.citedby3
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorSingh D.en_US
dc.contributor.authorNasif M.S.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid57191191317en_US
dc.contributor.authorid55188481100en_US
dc.date.accessioned2023-05-29T08:12:34Z
dc.date.available2023-05-29T08:12:34Z
dc.date.issued2020
dc.description.abstractFrequent boiler tube 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. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler. � Faculty of Computer Science and Information Technology.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.22452/mjcs.vol33no2.4
dc.identifier.epage151
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85090835871
dc.identifier.spage133
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090835871&doi=10.22452%2fmjcs.vol33no2.4&partnerID=40&md5=bb9265d347123db2f89b34d5ad44f663
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25674
dc.identifier.volume33
dc.publisherFaculty of Computer Science and Information Technologyen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleMalaysian Journal of Computer Science
dc.titleAdoption of intelligent computational techniques for steam boilers tube leak tripen_US
dc.typeArticleen_US
dspace.entity.typePublication
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