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Optimization of cerbera manghas biodiesel production using artificial neural networks integrated with ant colony optimization

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dc.citedby18
dc.contributor.authorSilitonga A.S.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorShamsuddin A.H.en_US
dc.contributor.authorOng H.C.en_US
dc.contributor.authorMilano J.en_US
dc.contributor.authorKusumo F.en_US
dc.contributor.authorSebayang A.H.en_US
dc.contributor.authorDharma S.en_US
dc.contributor.authorIbrahim H.en_US
dc.contributor.authorHusin H.en_US
dc.contributor.authorMofijur M.en_US
dc.contributor.authorRahman S.M.A.en_US
dc.contributor.authorid39262559400en_US
dc.contributor.authorid56997615100en_US
dc.contributor.authorid35779071900en_US
dc.contributor.authorid55310784800en_US
dc.contributor.authorid57052617200en_US
dc.contributor.authorid56611974900en_US
dc.contributor.authorid39262519300en_US
dc.contributor.authorid57217370281en_US
dc.contributor.authorid57196724785en_US
dc.contributor.authorid26428224700en_US
dc.contributor.authorid57204492012en_US
dc.contributor.authorid57201359295en_US
dc.date.accessioned2023-05-29T07:23:04Z
dc.date.available2023-05-29T07:23:04Z
dc.date.issued2019
dc.descriptionAnt colony optimization; Esters; Kinetic theory; Neural networks; Physicochemical properties; Transesterification; Artificial neural network models; Biodiesel production; Cerbera manghas oil; Experimental values; Kinetic study; Process parameters; Transesterification process; Trial and error; Biodieselen_US
dc.description.abstractOptimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3811
dc.identifier.doi10.3390/en12203811
dc.identifier.issue20
dc.identifier.scopus2-s2.0-85074968976
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074968976&doi=10.3390%2fen12203811&partnerID=40&md5=a8e209ed2b83c6eef77e8d38bf0fc843
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24373
dc.identifier.volume12
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleOptimization of cerbera manghas biodiesel production using artificial neural networks integrated with ant colony optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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