Publication:
A Machine Learning Approach for Fire-Fighting Detection in the Power Industry

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dc.citedby1
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorAl-Bazi A.en_US
dc.contributor.authorAl-Hadeethi R.H.en_US
dc.contributor.authorVictor M.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid35098298500en_US
dc.contributor.authorid57412220300en_US
dc.contributor.authorid57526587800en_US
dc.date.accessioned2023-05-29T09:09:58Z
dc.date.available2023-05-29T09:09:58Z
dc.date.issued2021
dc.descriptionCoal combustion; Coal fired power plant; Coal industry; Coal storage; Fires; Forecasting; Fossil fuel power plants; Multilayer neural networks; Sensitivity analysis; Spontaneous combustion; Clinker formation prediction model; Complex Processes; Fighting detections; Fire fighting; Machine learning approaches; Power industry; Prediction modelling; Reserved coals; Spontaneous combustion of coals; Storage yards; Coalen_US
dc.description.abstractCoal kept in the coal storage yard spontaneously catches on fire, which results in wastage and can even cause a massive fire to break out. This phenomenon is known as the spontaneous combustion of coal. It is a complex process that has non-linear relationships between its causing variables. Preventive measures to prevent the fire from spreading to other coal piles in the vicinity have already been implemented. However, the predictive aspect before the fire occurs is of great necessity for the power generation sector. This research investigates various prediction models for spontaneous coal combustion, explicitly selecting input and output parameters to identify a proper clinker formation prediction model. Feed-Forward Neural Network (FFNN) is proposed as a proper prediction model. Two Hidden Layers (2HL) network is found to be the best with 5 minutes prediction capability. A sensitivity analysis study is also conducted to determine the influence of random input variables on their respective response variables. � 2021 Jordan Journal of Mechanical and Industrial Engineering. All rights reserveden_US
dc.description.natureFinalen_US
dc.identifier.epage482
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85124127127
dc.identifier.spage475
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124127127&partnerID=40&md5=372470b39cf48201179ce84967efc92c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26397
dc.identifier.volume15
dc.publisherHashemite Universityen_US
dc.sourceScopus
dc.sourcetitleJordan Journal of Mechanical and Industrial Engineering
dc.titleA Machine Learning Approach for Fire-Fighting Detection in the Power Industryen_US
dc.typeArticleen_US
dspace.entity.typePublication
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