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Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine

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dc.citedby39
dc.contributor.authorSilitonga A.S.en_US
dc.contributor.authorHassan M.H.en_US
dc.contributor.authorOng H.C.en_US
dc.contributor.authorKusumo F.en_US
dc.contributor.authorid39262559400en_US
dc.contributor.authorid9232771700en_US
dc.contributor.authorid55310784800en_US
dc.contributor.authorid56611974900en_US
dc.date.accessioned2023-05-29T06:37:39Z
dc.date.available2023-05-29T06:37:39Z
dc.date.issued2017
dc.descriptionaccuracy assessment; biofuel; combustion; diesel engine; exhaust emission; industrial emission; machine learning; numerical model; parameter estimation; performance assessment; software; testing method; Jatropha curcas; biofuel; gasoline; analysis; chemistry; exhaust gas; Jatropha; machine learning; Biofuels; Gasoline; Jatropha; Machine Learning; Vehicle Emissionsen_US
dc.description.abstractThe purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805�0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259�2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy. � 2017, Springer-Verlag GmbH Germany.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-017-0141-9
dc.identifier.epage25405
dc.identifier.issue32
dc.identifier.scopus2-s2.0-85029602552
dc.identifier.spage25383
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85029602552&doi=10.1007%2fs11356-017-0141-9&partnerID=40&md5=73b05161c692246082c02fe2bd72606e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23068
dc.identifier.volume24
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleAnalysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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