Prediction of engine performance and exhaust emissions with different proportions of ethanol-gasoline blends using artificial neural networks

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dc.contributor.authorTekin, Mehmet
dc.contributor.authorSarıdemir, Suat
dc.date.accessioned2020-04-30T23:21:16Z
dc.date.available2020-04-30T23:21:16Z
dc.date.issued2019
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.descriptionWOS: 000470139500004en_US
dc.description.abstractThe main purpose of this study is to experimentally investigate the use of ANNs (artificial neural networks) modelling to predict engine power, torque and exhaust emissions of a spark ignition engine which operates with gasoline and methanol blends. For the ANN modelling, the standard back-propagation algorithm was found to be the optimal choice for training the model. Afterwards, the performance of the ANN predictions was evaluated with the experimental results by comparing the predictions. Fuel type and engine speed have been used as the input layer, while engine torque, power, exhaust emissions, Tex and BSFC have also been used separately as the output layer. It was found that the ANN model is able to predict the engine performance, exhaust emissions, Tex and BSFC with a correlation coefficient of 0.9991887425, 0.9990868573, 0.9986749623, 0.9988624137, 0.9976761492, 0.9992943894 and 0.9978899033 for the Power, Torque, CO, CO2, HC, Tex and BSFC for testing data, respectively.en_US
dc.identifier.doi10.1080/01430750.2017.1410225en_US
dc.identifier.endpage476en_US
dc.identifier.issn0143-0750
dc.identifier.issn2162-8246
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage470en_US
dc.identifier.urihttps://doi.org/10.1080/01430750.2017.1410225
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4167
dc.identifier.volume40en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofInternational Journal Of Ambient Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectethanolen_US
dc.subjectemissionsen_US
dc.subjectperformanceen_US
dc.titlePrediction of engine performance and exhaust emissions with different proportions of ethanol-gasoline blends using artificial neural networksen_US
dc.typeArticleen_US

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