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Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks

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dc.citedby9
dc.contributor.authorRamalingam K.en_US
dc.contributor.authorVenkatesan E.P.en_US
dc.contributor.authorVellaiyan S.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorSharifpur M.en_US
dc.contributor.authorYasir A.S.H.M.en_US
dc.contributor.authorSaleel C.A.en_US
dc.contributor.authorid57307229100en_US
dc.contributor.authorid57221721602en_US
dc.contributor.authorid56695172500en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid23092177300en_US
dc.contributor.authorid58518504200en_US
dc.contributor.authorid57197875592en_US
dc.date.accessioned2024-10-14T03:18:03Z
dc.date.available2024-10-14T03:18:03Z
dc.date.issued2023
dc.description.abstractThis study aims to derive bioenergy from waste lather fat and citronella grass. Lather fat oil (LFO), citronella grass oil (CGO), a mixture of leather fat oil and citronella grass oil (LFCGO), and a nano-additive-incorporated mixture of lather fat oil and citronella grass oil (NFCO) were synthesized and used in diesel engines as the novelty of this study. ASTM standards were used to investigate and guarantee the fuel's properties. According to the experimental report, the nanoadditive's brake thermal efficiency and brake-specific fuel consumption were more comparable to diesel fuel. Compared to diesel, the NFCO blend reduced hydrocarbon, carbon monoxide, and particulate emissions by 6.48%, 12.33%, and 16.66%, respectively, while carbon dioxide and oxides of nitrogen emissions increased. The experiment's outcomes were verified using an artificial neural network (ANN). The trained model exhibits a remarkable coefficient of determination of 98%, with high R values varying from 0.9075 to 0.9998 and low mean absolute percentage error values ranging from 0.97% to 4.24%. Based on the experimental findings and validation report, it can be concluded that NFCO is an efficient diesel fuel substitute. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.psep.2023.07.085
dc.identifier.epage1248
dc.identifier.scopus2-s2.0-85166779614
dc.identifier.spage1234
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85166779614&doi=10.1016%2fj.psep.2023.07.085&partnerID=40&md5=632146e4bac7592d56ea15be0b3bfab5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34122
dc.identifier.volume177
dc.pagecount14
dc.publisherInstitution of Chemical Engineersen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleProcess Safety and Environmental Protection
dc.subjectLather fat oil
dc.subjectNano additive
dc.subjectNOx emission
dc.subjectPeel oil
dc.subjectWaste to energy
dc.subjectASTM standards
dc.subjectBiodiesel
dc.subjectBiomass
dc.subjectBrakes
dc.subjectCarbon dioxide
dc.subjectCarbon monoxide
dc.subjectDiesel engines
dc.subjectFuel additives
dc.subjectNeural networks
dc.subjectThermal efficiency
dc.subjectBio-energy
dc.subjectBiomass-based fuels
dc.subjectCompression ignition engine
dc.subjectLather fat oil
dc.subjectNano additives
dc.subjectNOx emissions
dc.subjectPeel oil
dc.subjectSynthesised
dc.subjectWaste biomass
dc.subjectWaste to energy
dc.subjectDiesel fuels
dc.titleSubstitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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