A Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engine

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dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.date.accessioned2024-05-19T14:42:32Z
dc.date.available2024-05-19T14:42:32Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractTo lower the carbon dioxide and other emissions from a single cylinder common rail direct injection (CRDI) engine, it is important to investigate the combinations of several methods. Lemon peel oil (LPO) and camphor oil (CMO), which are low carbon content biofuels, are the methods that are used and are induced by hydrogen in the intake manifold and zeolite-based after-treatment system. At full load, the injection of hydrogen decreased CO2 and smoke emissions by 39.7% and 49%, respectively. Even though the NO emission increases with hydrogen induction, it was decreased with zeolite after-treatment system. Predictions can be made using machine learning techniques, which will reduce the amount of time and money needed for engine trials. This work focuses on the prediction of engine emissions like CO2, Nitrogen Oxides (NO), Smoke, Brake Thermal Efficiency (BTE), Hydrocarbons (HC) using the ensemble learning techniques. The predictions are made using the ensemble learning methods like Extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (LGBM), CatBoost, and Random Forest (RF). The CatBoost model has produced high accuracy predictions which was followed by XGBoost, RF and LightGBM models. The predicted and actual values are compared each other and the performance of the algorithms were analysed using the evaluation metrics like R-Square(R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).en_US
dc.identifier.doi10.1016/j.fuel.2023.127578
dc.identifier.issn0016-2361
dc.identifier.issn1873-7153
dc.identifier.scopus2-s2.0-85147411122en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.fuel.2023.127578
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5254
dc.identifier.volume341en_US
dc.identifier.wosWOS:000963981800001en_US
dc.identifier.wosqualityN/Aen_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.snmz20240519_kaen_US
dc.subjectHydrogen Energyen_US
dc.subjectLemon Peel Oilen_US
dc.subjectCamphor Oilen_US
dc.subjectEmissionsen_US
dc.subjectMachine Learningen_US
dc.subjectEnsemble Learningen_US
dc.titleA Machine Learning Approach for Carbon di oxide and Other Emissions Characteristics Prediction in a Low Carbon Biofuel-Hydrogen Dual Fuel Engineen_US
dc.typeArticleen_US

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