Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–hydrogen fuelled dual fuel engine

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dc.authoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506en_US
dc.authoridEdwin Geo Varuvel / 0000-0002-7303-3984en_US
dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 57973036900en_US
dc.authorscopusidEdwin Geo Varuvel / 25225283500en_US
dc.authorwosidFemilda Josephin Joseph Shobana Bai / AGG-4255-2022en_US
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022en_US
dc.contributor.authorJoseph Shobana Bai, Femilda Josephin
dc.contributor.authorShanmugaiah, Kaliraj
dc.contributor.authorSonthalia, Ankit
dc.contributor.authorDevarajan, Yuvarajan
dc.contributor.authorVaruvel, Edwin Geo
dc.date.accessioned2023-01-07T13:23:57Z
dc.date.available2023-01-07T13:23:57Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn this research work, performance and emission parameters of wheat germ oil (WGO) -hydrogen dual fuel was investigated experimentally and these parameters were predicted using different machine learning algorithms. Initially, hydrogen injection with 5%, 10% and 15% energy share were used as the dual fuel strategy with WGO. For WGO +15% hydrogen energy share the NO emission is 1089 ppm, which is nearly 33% higher than WGO at full load. As hydrogen has higher flame speed and calorific value and wider flammability limit which increases the combustion temperature. Thus, the reaction between nitrogen and oxygen increases thereby forming more NO. Smoke emission for WGO +15% hydrogen energy share is 66%, which is 15% lower compared to WGO, since the heat released in the pre-mixed phase of combustion is increased to a maximum with higher hydrogen energy share compared to WGO. Different applications including internal combustion engines have used machine learning approaches for predictions and classifications. In the second phase various machine learning techniques namely Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Machines (SVM)) were used to predict the emission characteristics of the engine operating in dual fuel mode. The machine learning models were trained and tested using the experimental data. The most effective model was identified using performance metrics like R-Squared (R2) value, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The result shows that the prediction by MLR model was closest to the experimental results. © 2022 Hydrogen Energy Publications LLCen_US
dc.identifier.citationBai, F. J. J. S., Shanmugaiah, K., Sonthalia, A., Devarajan, Y., & Varuvel, E. G. (2022). Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–Hydrogen fuelled dual fuel engine. International Journal of Hydrogen Energy.en_US
dc.identifier.doi10.1016/j.ijhydene.2022.11.101en_US
dc.identifier.issn0360-3199en_US
dc.identifier.scopus2-s2.0-85144852791en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ijhydene.2022.11.101
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3817
dc.identifier.wosWOS:001035362500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorJoseph Shobana Bai, Femilda Josephin
dc.institutionauthorVaruvel, Edwin Geo
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInternational journal of hydrogen energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDual Fuel Engineen_US
dc.subjectEmissionsen_US
dc.subjectHydrogen Energyen_US
dc.subjectMachine Learningen_US
dc.subjectPerformanceen_US
dc.subjectWheat Germ Oilen_US
dc.titleApplication of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–hydrogen fuelled dual fuel engineen_US
dc.typeArticleen_US

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