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A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant

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dc.citedby4
dc.contributor.authorBunyamin M.A.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorAziz N.L.A.A.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorKamal M.F.en_US
dc.contributor.authorid55812855600en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55812399400en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid55812401300en_US
dc.date.accessioned2023-12-29T07:45:13Z
dc.date.available2023-12-29T07:45:13Z
dc.date.issued2013
dc.description.abstractThis paper presents a new approach of gas emission estimation in power generation plant using a hybrid Genetic Algorithm (GA) and Linear Regression (LR) (denoted as GA-LR). The LR is one of the approaches that model the relationship between an output dependant variable, y, with one or more explanatory variables or inputs which denoted as x. It is able to estimate unknown model parameters from inputs data. On the other hand, GA is used to search for the optimal solution until specific criteria is met causing termination. These results include providing good solutions as compared to one optimal solution for complex problems. Thus, GA is widely used as feature selection. By combining the LR and GA (GA-LR), this new technique is able to select the most important input features as well as giving more accurate prediction by minimizing the prediction errors. This new technique is able to produce more consistent of gas emission estimation, which may help in reducing population to the environment. In this paper, the study's interest is focused on nitrous oxides (NOx) prediction. The results of the experiment are encouraging. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12101
dc.identifier.doi10.1088/1755-1315/16/1/012101
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84881088317
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84881088317&doi=10.1088%2f1755-1315%2f16%2f1%2f012101&partnerID=40&md5=49efb57d7428274ab7a728d8c053b1f3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30171
dc.identifier.volume16
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.subjectEstimation
dc.subjectGas emissions
dc.subjectGenetic algorithms
dc.subjectNitrogen oxides
dc.subjectOptimal systems
dc.subjectAccurate prediction
dc.subjectEmission estimation
dc.subjectExplanatory variables
dc.subjectHybrid genetic algorithms
dc.subjectModel parameters
dc.subjectOptimal solutions
dc.subjectPower generation plants
dc.subjectPrediction errors
dc.subjectcomparative study
dc.subjectelectricity generation
dc.subjecterror analysis
dc.subjectestimation method
dc.subjectgenetic algorithm
dc.subjectindustrial emission
dc.subjectnumerical model
dc.subjectoptimization
dc.subjectparameterization
dc.subjectregression analysis
dc.subjectForecasting
dc.titleA hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation planten_US
dc.typeConference paperen_US
dspace.entity.typePublication
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