A comparative analysis of advanced machine learning models for the prediction of combustion, emission and performance characteristics using endoscopic combustion flame image of a pine oil–gasoline fuelled spark ignition engine

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dc.authorscopusidEdwin Geo Varuvel / 25225283500
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022
dc.contributor.authorGodwin, D. Jesu
dc.contributor.authorVaruvel, Edwin Geo
dc.contributor.authorJesu Martin, M. Leenus
dc.contributor.authorJasmine R, Anita
dc.contributor.authorJosephin JS, Femilda
dc.date.accessioned2025-04-16T19:29:21Z
dc.date.available2025-04-16T19:29:21Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractThis research focuses on using machine learning to predict the spark ignition engine's combustion, performance, and emission parameters with bio-fuel blends such as pine oil blend, which significantly diminishes the environmental impact of traditional fuels, reduces the limitations of repeated engine experimentation and addresses the nonlinearities in engine test results contributing to sustainable cleaner fuel and energy solutions. The models used were Ensemble Decision Tree Bagging, Ensemble Least Squares Boosting, Gaussian Process Regression and Support Vector Machine Regression, with good generalization ability. Brake Specific Fuel Consumption data from the test engine trials and endoscopic image flame area data after spark timing at different crank angles (320, 400, 480, 560, and 640 after Spark Timing) were fed into the machine-learning models as predictors. The response variables were Brake thermal efficiency, Unburnt Hydrocarbons, Carbon monoxide, Carbon dioxide, Oxides of nitrogen, maximum In-cylinder pressure, and maximum heat release rate. The bootstrap technique was used to generate numerous datasets from the experimental data for data-driven model training and tested using both interpolative and extrapolative data. The experimental and predicted values for all these algorithms were subjected to repeated hyperparameter optimization trials and the best machine learning method was identified using the performance and error metrics. The Ensemble Least Squares Boost model showed the overall best correlation (R2) in the range of 0.97–0.99 for gasoline and pine oil PN20 blend for the predicted versus experimental engine parameters. The root-mean-squared error (RMSE) at maximum load ranged between 0.0086 and 0.3044 for gasoline and 0.0049–0.2046 for the Pine oil PN20 fuel blend respectively. Therefore, employing an Ensemble Least Squares Boosting machine learning framework can effectively predict the characteristics of gasoline engines using pine oil and blends. This approach serves as a virtual engine model, efficiently overcoming the limitations and complexities inherent in conventional engine experiments. © 2024 Elsevier Ltd
dc.identifier.citationGodwin, D. J., Varuvel, E. G., Martin, M. L. J., & JS, F. J. (2024). A comparative analysis of advanced machine learning models for the prediction of combustion, emission and performance characteristics using endoscopic combustion flame image of a pine oil–gasoline fuelled spark ignition engine. Journal of Cleaner Production, 484, 144284.
dc.identifier.doi10.1016/j.jclepro.2024.144284
dc.identifier.issn09596526
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.jclepro.2024.144284
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6048
dc.identifier.volume484
dc.identifier.wosWOS:001374138400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorVaruvel, Edwin Geo
dc.institutionauthoridEdwin Geo Varuvel / 0000-0002-7303-3984
dc.language.isoen
dc.publisherElsevier Ltd.
dc.relation.ispartofJournal of Cleaner Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBio-fuel Blend
dc.subjectEnsemble Learning
dc.subjectMachine Learning
dc.subjectPine Oil
dc.subjectPrediction
dc.titleA comparative analysis of advanced machine learning models for the prediction of combustion, emission and performance characteristics using endoscopic combustion flame image of a pine oil–gasoline fuelled spark ignition engine
dc.typeArticle

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