Efficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and diesel

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dc.authoridSubramanian, Balaji/0000-0002-1298-9288
dc.authoridS, Naveen Venkatesh/0000-0002-4034-8859
dc.authoridVijayaragavan, Mathanraj/0000-0002-4173-8306
dc.authoridVaruvel, Edwin Geo/0000-0002-7303-3984
dc.authorwosidSubramanian, Balaji/GLQ-8228-2022
dc.authorwosidS, Naveen Venkatesh/GNM-5892-2022
dc.authorwosidVijayaragavan, Mathanraj/JJE-8956-2023
dc.contributor.authorVenkatesh, S. Naveen
dc.contributor.authorSugumaran, V
dc.contributor.authorThangavel, Venugopal
dc.contributor.authorBalaji, P. Arun
dc.contributor.authorVijayaragavan, Mathanraj
dc.contributor.authorSubramanian, Balaji
dc.contributor.authorJosephin, J. S. Femilda
dc.date.accessioned2024-05-19T14:40:58Z
dc.date.available2024-05-19T14:40:58Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractEmission created by combustion of fossil fuels are a major concern of the world for the past few decades. The stringent emission norms have impacted the automobile manufacturers to work on exhaust emissions and its impact. This research focused on using machine learning regression models to evaluate the efficacy of experimental results for a dual fuel compression ignition (CI) engine operating on hydrogen and diesel. In the present study, engine emissions were estimated using 29 regression algorithms. A total of 5 input data namely, concentration of hydrogen, engine load, diesel intake, speed and equivalence ratio were considered in the study to estimate various emissions like oxides of nitrogen (NOx), carbon dioxide (CO2), hydrocarbon (HC) and smoke. Correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error were adopted as the performance metrics in the present study. Amongst the algorithms considered, pace regression, radial basis function regressor, multilayer perceptron regressor and alternating model tree produced the highest correlation coefficient of 0.9985, 0.8958, 0.9950 and 0.9256 in estimating the engine emissions like CO2, smoke, NOx and HC respectively. Additionally, an attempt was made to establish an individual algorithm that can estimate all the emissions was identified as multilayer perceptron regressor with correlation coefficient values of 0.9977 (CO2), 0.9950 (NOx), 0.8501(smoke) and 0.8731(HC) respectively. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijhydene.2023.03.477
dc.identifier.endpage39611en_US
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue99en_US
dc.identifier.scopus2-s2.0-85164722238en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage39599en_US
dc.identifier.urihttps://doi.org10.1016/j.ijhydene.2023.03.477
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5044
dc.identifier.volume48en_US
dc.identifier.wosWOS:001114543600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofInternational Journal of Hydrogen Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDual Fuel Engineen_US
dc.subjectHydrogenen_US
dc.subjectPace Regressionen_US
dc.subjectMlp Regressoren_US
dc.subjectRbf Regressoren_US
dc.titleEfficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and dieselen_US
dc.typeArticleen_US

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