Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas

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dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 59417834100
dc.authorscopusidEdwin Geo Varuvel / 25225283500
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JTQ-1812-2023
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022
dc.contributor.authorS, Priya
dc.contributor.authorFeenita, C.
dc.contributor.authorGoel, Uday
dc.contributor.authorT, Manoranjitham
dc.contributor.authorDuraisamy, Boopathi
dc.contributor.authorSubramanian, Balaji
dc.contributor.authorGaneshan, Kavitha
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorAlbeshr, Mohammed F.
dc.contributor.authorPugazhendhi, Arivalagan
dc.contributor.authorVaruvel, Edwin Geo
dc.date.accessioned2025-04-17T10:42:06Z
dc.date.available2025-04-17T10:42:06Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractInternal combustion engines (ICEs) have long been essential in both the transportation and industrial sectors, providing primary power for vehicles, ships, and machines globally. Optimising the efficiency of ICEs is vital for decreasing their environmental impart, as increased fuel efficiency and lower emissions play a significant role in mitigating the effects of climate change as well as improving air quality. This study employed 15 regression algorithms and machine learning approaches to analyse and anticipate the performance parameters of ICEs that run on hydrogen-diesel in dual fuel mode. The input parameters include engine torque, speed, hydrogen flow rate, brake power and diesel energy share to hydrogen supply and the output parameters are brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and actual air intake. The model's performance is evaluated using five different performance metrics. Among the studied algorithms, the RANSAC Regressor demonstrated exceptional predictive capability, reaching an R-squared value of 0.999, a mean squared error (MSE) of 0.0064, a root mean square error (RMSE) of 0.08, and a mean absolute error (MAE) of 0.057. These outcomes show the algorithm's accuracy and precision in capturing the complicated data of engine system. The equivalency ratio, volumetric efficiency, brake thermal efficiency, brake specific fuel consumption, and actual air intake are among the critical performance outputs that are optimised by utilizing key input parameters like engine load, rotational speed, hydrogen flow rate, brake power, and the diesel fuel energy share. This study highlights the significant potential of machine learning in optimising ICE performance, offering a reliable alternative to traditional experimental analysis by reducing both risk and economic costs. The research findings also support the paradigm shift towards intelligent and sustainable energy systems by compellingly advocating for the inclusion of data-driven methodologies in contemporary engine design and operational methods
dc.description.sponsorshipKing Saud University
dc.identifier.citationPriya, S., Feenita, C., Goel, U., Manoranjitham, T., Duraisamy, B., Subramanian, B., ... & Varuvel, E. G. (2024). Comparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas. International Journal of Hydrogen Energy.
dc.identifier.doi10.1016/j.ijhydene.2024.12.238
dc.identifier.endpage13
dc.identifier.issn03603199
dc.identifier.scopus2-s2.0-85214254749
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.ijhydene.2024.12.238
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6203
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthorVaruvel, Edwin Geo
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.institutionauthoridEdwin Geo Varuvel / 0000-0002-7303-3984
dc.language.isoen
dc.publisherElsevier ltd
dc.relation.ispartofInternational journal of hydrogen energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDual Fuel Engine
dc.subjectHydrogen
dc.subjectMachine Learning Algorithm
dc.subjectRegression Models
dc.titleComparative analysis of regression models to predict the performance of the dual fuel engine operating on diesel and hydrogen gas
dc.typeArticle

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