Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles-diesel blends using different machine learning algorithms

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dc.contributor.authorAgbulut, Umit
dc.contributor.authorGurel, Ali Etem
dc.contributor.authorSandemir, Suat
dc.date.accessioned2021-12-01T18:47:13Z
dc.date.available2021-12-01T18:47:13Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractDeep learning (DL), Artificial Neural Network (ANN), Kernel Nearest Neighbor (k-NN), and Support Vector Machine (SVM) have been applied to numerous fields owing to their high-accuracy and ability to analyze the non-linear problems. In this study, these machine learning algorithms (MLAs) are used to predict emission and performance characteristics of a CI engine fuelled with various metal-oxide based nano particles (Al2O3, CuO, and TiO2) at a mass fractions of 200 ppm. Assessed parameters in the study are carbon dioxide (CO), nitrogen oxide (NOx), exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE). To evaluate the success of algorithms, four metrics (R-2, RMSE, rRMSE, and MBE) are discussed in detail. Tests performed at varying engine speeds from 1500 rpm to 3400 rpm with the intervals of 100 rpm. The addition of nanoparticles simultaneously reduced CO and NOx emissions because they ensured more complete combustion thanks to their inherent oxygen, the higher surface to volume ratio, superior thermal conductivities and their catalytic activity role. Further, the nano-sized particles ensured an accelerated heat transfer from the combustion chamber. In comparison with that of neat diesel fuel, the reduction in NOx is found to be 3.28, 7.53, and 10.05%, and the reduction in CO is found to be 8.3, 11.6, and 15.5% for TiO2, Al2O3, and CuO test fuels, respectively. Moreover, the presence of nanoparticles in test fuels has improved engine performance. As compared with those of neat diesel fuel, the doping of nanoparticles drops the BSFC value by 5.54, 7.89, and 9.96% for TiO2, CuO, and Al2O3, respectively, and enhanced BTE value to be 6.15, 8.87, and 11.23% for TiO2, CuO, and Al2O3, respectively. On the other hand, it can be said that all algorithms presented very satisfying results in the prediction of CI engine responses. All R-2 has changed between 0.901 and 0.994, and DL has given the highest R-2 value for each engine response. In terms of rRMSE, all results (except for one result in k-NN) are categorized as excellent according to the classification in the literature. Considering all metrics together, DL is giving the best results in the prediction of engine responses for the dataset used in this paper. Then it is closely followed by ANN, SVM, and k-NN algorithms, respectively. In conclusion, this paper is proving that the nanoparticle addition for ICEs is significantly dropping the exhaust pollutants, and improving the engine performance, and further the results can be successfully predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipDuzce University Scientific Research Projects Coordination UnitDuzce University [2020.07.04.1097, 2019.07.04.1049]; Duzce UniversityDuzce Universityen_US
dc.description.sponsorshipThis present work is being funded by Duzce University Scientific Research Projects Coordination Unit with the project numbers 2020.07.04.1097, and 2019.07.04.1049. The authors thank Duzce University for its financial support, and express special thanks to PhD Candidate Melahat Sevgul Bakay from Biomedical Engineering Department at Duzce University for her valuable suggestions in the prediction stages of the paper.en_US
dc.identifier.doi10.1016/j.energy.2020.119076
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85094937244en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.energy.2020.119076
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10189
dc.identifier.volume215en_US
dc.identifier.wosWOS:000596172500006en_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.subjectMachine learningen_US
dc.subjectPredictionen_US
dc.subjectEngine performanceen_US
dc.subjectEmissionen_US
dc.subjectNanoparticleen_US
dc.subjectNanodieselen_US
dc.subjectGlobal Solar-Radiationen_US
dc.subjectArtificial Neural-Networksen_US
dc.subjectSupport Vector Machineen_US
dc.subjectExhaust Emissionsen_US
dc.subjectThermal-Conductivityen_US
dc.subjectRegression-Modelsen_US
dc.subjectExpanded Perliteen_US
dc.subjectVegetable-Oilen_US
dc.subjectBiodieselen_US
dc.subjectCombustionen_US
dc.titleExperimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles-diesel blends using different machine learning algorithmsen_US
dc.typeArticleen_US

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