Performance and Exhaust Emission Prediction of a SI Engine Fueled with I-amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based Optimization

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dc.authoridUslu, Samet/0000-0001-9118-5108
dc.contributor.authorUslu, Samet
dc.contributor.authorCelik, Mustafa 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 this study, effects of i-amyl alcohol/gasoline fuel blends on spark ignition (SI) engine performance and emissions were investigated experimentally, predicted by Artificial Neural Network (ANN) and optimized with Response Surface Methodology (RSM). Test engine was operated with pure gasoline and gasoline-isoamyl alcohol (isopentanol) fuel mixtures with different proportion at different engine speeds and various compression ratios (CR). With respect to obtained data from experiments, an ANN model, which is an Artificial Intelligence (AI) application, has been developed to estimate outputs such as brake mean effective pressure (BMEP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), nitrogen oxides (NOx), hydrocarbon emission (HC) and carbon monoxide (CO) according to CR, fuel blending ratio (by vol.%) and engine speed (rpm). In addition, RSM was applied to find suitable engine operating conditions. According to results, the ANN model can estimate performance and emission parameters of engine by correlation coefficient (R-2) between 0.94 and 0.99. It is seen that the max. mean relative error (MRE) is less than 7% compared with outcomes obtained from tests. The RSM study demonstrated that, i-AA ratio of 15% at 8.31 CR and 2957.58 rpm engine speed are the optimal engine operating parameters. In this way, the ANN model with RSM support was found to be an effective tool for predicting and optimizing engine outputs with minimum test.en_US
dc.identifier.doi10.1016/j.fuel.2019.116922
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85077012662en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.fuel.2019.116922
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4663
dc.identifier.volume265en_US
dc.identifier.wosWOS:000508914300028en_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.subjectAlcohol blendsen_US
dc.subjectOptimization approachen_US
dc.subjectPredictionen_US
dc.subjectSpark ignition engineen_US
dc.titlePerformance and Exhaust Emission Prediction of a SI Engine Fueled with I-amyl Alcohol-Gasoline Blends: An ANN Coupled RSM Based Optimizationen_US
dc.typeArticleen_US

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