Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization

Basit Öğe Kaydı
dc.authoridUslu, Samet/0000-0001-9118-5108
dc.authoridAYDIN, MUSTAFA/0000-0002-6187-6722
dc.contributor.authorAydin, Mustafa
dc.contributor.authorUslu, Samet
dc.contributor.authorCelik, M. Bahattin
dc.date.accessioned2024-09-29T15:57:11Z
dc.date.available2024-09-29T15:57:11Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn the present study, the performance and emission parameters of a single cylinder diesel engine powered by biodiesel-diesel fuel blends were predicted by Artificial Neural Network (ANN) and optimized by Response Surface Methodology (RSM). The data to be used for ANN and RSM applications were obtained by using biodiesel/diesel fuel blends at different engine loads and various injection pressures. ANN model has been developed to predict the outputs such as brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO) and smoke regarding engine load, biodiesel ratio and injection pressure. A feed-forward multi-layer perceptron network is used to show the correlation among the input factors and the output factors. The RSM is applied to find the optimum engine operating parameters with the purpose of simultaneous reduction of emissions, EGT, BSFC and increase BTE. The obtained results reveal that the ANN can correctly model the exhaust emission and performance parameters with the regression coefficients (R-2) between 0.8663 and 0.9858. It is seen that the maximum mean relative error (MRE) is less than 10%, compared with the experimental results. The RSM study demonstrated that, biodiesel ratio of 32% with 816-W engine load and 470 bar injection pressure are the optimum engine operating parameters. It is found that the ANN with RSM support is a good tool for predict and optimize of diesel engine parameters powered with diesel/biodiesel mixtures.en_US
dc.identifier.doi10.1016/j.fuel.2020.117472
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85080058229en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2020.117472
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4665
dc.identifier.volume269en_US
dc.identifier.wosWOS:000520021800064en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofFuelen_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.subjectBiodieselen_US
dc.subjectOptimizationen_US
dc.subjectPredictionen_US
dc.subjectDiesel engineen_US
dc.titlePerformance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimizationen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu