Development of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil - Hydrogen on PCCI operation

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dc.authoridBalasubramanian, Dhinesh/0000-0003-2687-9095
dc.authoridVaruvel, Edwin Geo/0000-0002-7303-3984
dc.authorwosidBalasubramanian, Dhinesh/B-2911-2016
dc.authorwosidDevarajan, Yuvarajan/AAN-7100-2020
dc.contributor.authorVaruvel, Edwin Geo
dc.contributor.authorSeetharaman, Sathyanarayanan
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorDevarajan, Yuvarajan
dc.contributor.authorBalasubramanian, Dhinesh
dc.date.accessioned2024-05-19T14:50:28Z
dc.date.available2024-05-19T14:50:28Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIdentifying the suitable alternative fuel and optimum blend concentration for diesel engine combustion is critical as most biodiesel emits excess smoke and has a lower thermal efficiency due to its high viscosity and carbon residue. In the previous work, rubber seed oil was tested in a single-cylinder diesel engine, and its performance and emission results were compared with those of pure diesel, an RSO-diesel (70:30 by volume) blend, RSOmethyl ester, RSO-diethyl ether, RSO-ethanol, and RSO-hydrogen in a dual fuel operation. The testing was performed at a constant speed of 1500 rpm, with the engine loads varying at 25% step intervals. Results showed that smoke and nitrogen oxides were significantly reduced for RSO, and engine performance was enhanced when RSO was operated with hydrogen and diethyl ether in dual fuel mode. In this study, the experimental results were employed to develop an artificial neural network and response surface methodology model. Brake thermal efficiency, rate of pressure rise, carbon monoxide, hydrocarbon, oxides of nitrogen, and smoke were predicted using response surface methodology and artificial neural network. Though artificial neural network produced the best R2 values (0.87264-0.99929), mean absolute percentage error was relatively lesser in response surface methodology. Thus, the authors conclude that response surface methodology is the best suitable artificial intelligence tool to optimize the engine for accomplishing desired responses.en_US
dc.identifier.doi10.1016/j.energy.2023.129110
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85172087810en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.energy.2023.129110
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5721
dc.identifier.volume283en_US
dc.identifier.wosWOS:001084147000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDiesel-Engineen_US
dc.subjectExhaust Emissionsen_US
dc.subjectDual-Fuelen_US
dc.subjectBiodieselen_US
dc.subjectPerformanceen_US
dc.subjectGasen_US
dc.subjectBlendsen_US
dc.subjectCombustionen_US
dc.subjectAnnen_US
dc.subjectVibrationen_US
dc.titleDevelopment of artificial neural network and response surface methodology model to optimize the engine parameters of rubber seed oil - Hydrogen on PCCI operationen_US
dc.typeArticleen_US

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