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

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dc.authorscopusid57204216513
dc.authorscopusid57203723320
dc.authorscopusid57223897850
dc.contributor.authorŞimşek, Süleyman
dc.contributor.authorUslu, Samet
dc.contributor.authorŞimşek, Hatice
dc.date.accessioned2022-05-11T14:47:59Z
dc.date.available2022-05-11T14:47:59Z
dc.date.issued2022
dc.departmentMeslek Yüksekokulları, Teknik Bilimler Meslek Yüksekokulu, Makine ve Metal Teknolojileri Bölümü
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 (R2) 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%. © 2021 Elsevier Ltd
dc.description.sponsorshipNo financial support was received from any institution or organization for this study.
dc.identifier.doi10.1016/j.energy.2021.122389
dc.identifier.issn0360-5442
dc.identifier.scopus2-s2.0-85117927779
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.energy.2021.122389
dc.identifier.urihttps://hdl.handle.net/20.500.11776/10582
dc.identifier.volume239
dc.indekslendigikaynakScopus
dc.institutionauthorŞimşek, Hatice
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofEnergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAnimal fat biodiesel
dc.subjectArtificial neural network
dc.subjectDiesel engine
dc.subjectPrediction
dc.subjectResponse surface methodology
dc.subjectAgricultural wastes
dc.subjectAnimals
dc.subjectBiodiesel
dc.subjectDiesel engines
dc.subjectForecasting
dc.subjectSurface properties
dc.subjectAnimal fat
dc.subjectAnimal fat biodiesel
dc.subjectAnimal wastes
dc.subjectCorrelation coefficient
dc.subjectNeural response
dc.subjectOptimum ratio
dc.subjectPercentage error
dc.subjectPerformance
dc.subjectResponse-surface methodology
dc.subjectWaste fat
dc.subjectNeural networks
dc.subjectartificial neural network
dc.subjectbiofuel
dc.subjectdiesel engine
dc.subjectnumerical model
dc.subjectprediction
dc.subjectresponse surface methodology
dc.subjectwaste technology
dc.titleProportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine
dc.typeArticle

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