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Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler

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dc.citedby1
dc.contributor.authorGurusingam P.en_US
dc.contributor.authorBasim Ismail F.en_US
dc.contributor.authorGunnasegaran P.en_US
dc.contributor.authorSundaram T.en_US
dc.contributor.authorid57196439549en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid35778031300en_US
dc.contributor.authorid57196438069en_US
dc.date.accessioned2023-05-29T06:37:42Z
dc.date.available2023-05-29T06:37:42Z
dc.date.issued2017
dc.descriptionCoal; Coal ash; Coal combustion; Coal fueled furnaces; Fly ash; Forecasting; Monitoring; Neural networks; Artificial neural network modeling; Coal fired plants; Coal-fired power plant; Conventional methods; Electricity demands; Incomplete combustion; Intelligent monitoring systems; Unburned carbon in fly ashes; Fossil fuel power plantsen_US
dc.description.abstractCoal fired power plant becoming preferable power plant type to support electricity demand mainly in Asia due to stable coal price and low maintenance. However, most coal fired plant operator struggle with condition where coal undergo incomplete combustion and produced unburned carbon where can be found in ashes especially in fly ash. Higher percentage of unburned carbon in fly ash reflects the lower efficiency of furnace and contributes to financial loses for plant operators. This problem also leads to technical issues such as slagging and clinkering and further reduces the efficiency of furnace. The plant operator determines the amount of unburned carbon by using conventional method and this proves be a challenge to identify and rectify the problem on day basis due time constraint to obtain results of unburned carbon. Thus in this paper, best Artificial Neural Network model was derived to develop intelligent monitoring system to predict unburned carbon level on more daily basis. By this model, the power producer can predict the unburned carbon level by using data in power plant to predict the unburned carbon level in short period of time. � The authors, published by EDP Sciences, 2017.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2003
dc.identifier.doi10.1051/matecconf/201713102003
dc.identifier.scopus2-s2.0-85033236050
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85033236050&doi=10.1051%2fmatecconf%2f201713102003&partnerID=40&md5=000df79491f0c15ea7b5c7755f2f4228
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23078
dc.identifier.volume131
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titleIntelligent monitoring system of unburned carbon of fly ash for coal fired power plant boileren_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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