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A support vector based CO2 gas emission prediction system for generation power plant

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dc.contributor.authorChen C.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorAlbert F.Y.C.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorid25824552100en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid56572305600en_US
dc.contributor.authorid22951210700en_US
dc.date.accessioned2023-05-29T06:38:39Z
dc.date.available2023-05-29T06:38:39Z
dc.date.issued2017
dc.description.abstractThe work presents an intelligent system Support Vector Regression Emission Monitoring System (SuVEMS) developed for Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Peninsular Malaysia for the prediction of harmful gas emissions from electricity generating power plants in Tuanku Jaafar Power Station (TJPS). The CO2, emissions is modelled on this work using Support Vector Regression (SVR), a statistical machine learning tool with a regression-based extension towards Support Vector Machines (SVMs). The gas is predicted using independent models and the gas prediction model is trained using feature subsets selected using the forward selection approach. The SuVEMS results are compared and measured the performance with the Continuous Emission Monitoring System, CEMS results. The SuVEMS results implemented at TJPS indicate that it has the ability for the online prediction with average prediction accuracy of 95%. � 2017 American Scientific Publishers All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1166/asl.2017.8875
dc.identifier.epage4522
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85023763610
dc.identifier.spage4518
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023763610&doi=10.1166%2fasl.2017.8875&partnerID=40&md5=ae2cfa8a998b657558472b2f09d6dfa4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23234
dc.identifier.volume23
dc.publisherAmerican Scientific Publishersen_US
dc.sourceScopus
dc.sourcetitleAdvanced Science Letters
dc.titleA support vector based CO2 gas emission prediction system for generation power planten_US
dc.typeArticleen_US
dspace.entity.typePublication
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