Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network

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dc.contributor.authorÇay, Yusuf
dc.contributor.authorKorkmaz, İbrahim
dc.contributor.authorÇiçek, Adem
dc.contributor.authorKara, Fuat
dc.date.accessioned2020-04-30T23:21:16Z
dc.date.available2020-04-30T23:21:16Z
dc.date.issued2013
dc.departmentDÜ, Düzce Meslek Yüksekokuluen_US
dc.descriptionKARA, Fuat/0000-0002-3811-3081en_US
dc.descriptionWOS: 000316432700019en_US
dc.description.abstractThis study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (air-fuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (Levenberg-Marquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.energy.2012.10.052en_US
dc.identifier.endpage186en_US
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage177en_US
dc.identifier.urihttps://doi.org/10.1016/j.energy.2012.10.052
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4166
dc.identifier.volume50en_US
dc.identifier.wosWOS:000316432700019en_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.subjectGasolineen_US
dc.subjectMethanolen_US
dc.subjectANNen_US
dc.subjectEngine performanceen_US
dc.subjectExhaust emissionsen_US
dc.titlePrediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural networken_US
dc.typeArticleen_US

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