Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine

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dc.authoridUslu, Samet/0000-0001-9118-5108
dc.contributor.authorSimsek, Suleyman
dc.contributor.authorUslu, Samet
dc.contributor.authorSimsek, Hatice
dc.date.accessioned2024-09-29T15:55:20Z
dc.date.available2024-09-29T15:55:20Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractInstead of many experimental studies made for the suitability of biodiesel for use in diesel engine, it has become easier to determine by fewer experiments with the development of computer applications. In this research, it was aimed to determine the optimum ratio of animal waste fat biodiesel (AWFBD) and the corresponding engine responses by using artificial neural network (ANN) and response surface methodology (RSM). In addition, a comparison was made with test results to evaluate the performance of ANN and RSM. According to the regression results obtained from RSM, absolute fraction of variance (R-2) values greater than 0.95 emerged for all answers. Correlation coefficient (R) values obtained from ANN were found to be higher than 0.97. The developed ANN model was able to predict engine responses with mean absolute percentage error (MAPE) in the range of 3.787-10.730%. MAPE values for RSM were obtained between 2.004 and 11.461%. Combined desirability factor obtained from RSM was found as 0.72288% and optimum engine parameters were found as 22% AWFBD ratio and 1350-Watt engine load. In addition, according to the verification test between the optimum results and the prediction results, it was concluded that there is a good agreement with a maximum error rate of 3.863%. (C) 2021 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.energy.2021.122389
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85117927779en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.energy.2021.122389
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4600
dc.identifier.volume239en_US
dc.identifier.wosWOS:000719389800002en_US
dc.identifier.wosqualityQ1en_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.subjectArtificial neural networken_US
dc.subjectResponse surface methodologyen_US
dc.subjectAnimal fat biodieselen_US
dc.subjectPredictionen_US
dc.subjectDiesel engineen_US
dc.titleProportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engineen_US
dc.typeArticleen_US

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