Tree-based ensemble regression models for emission prediction of a winter green oil-hydrogen dual-fuel engine with zeolite after-treatment
This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. Extra Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting...
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| Vydané v: | Renewable energy Ročník 257; s. 124726 |
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| Hlavní autori: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.02.2026
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| ISSN: | 0960-1481, 1879-0682 |
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| Abstract | This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. Extra Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and AdaBoost are the tree-based ensemble regression models used to predict the emission parameters under limited data conditions. The performance of the models was assessed through 5-fold cross-validation and a 20 % hold-out test method using R-Squared (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as the evaluation metrics. Among the five tree-based regression models Extra Trees Regressor performed better with highest R2 values in the range of 0.99966–0.99974 and the lowest error metrics for all the emission parameters and demonstrates the outstanding robustness and generalization ability of the model. The stronger consistency of extra trees across different test samples was demonstrated by absolute error heatmaps, while the model's accuracy was further validated by comparing actual and predicted values. The study's overall findings demonstrate the potential of tree-based ensemble learning, and extra trees in particular, as a lightweight, accurate and reliable tool for real-time emission prediction in low-carbon dual-fuel systems. |
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| AbstractList | This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. Extra Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and AdaBoost are the tree-based ensemble regression models used to predict the emission parameters under limited data conditions. The performance of the models was assessed through 5-fold cross-validation and a 20 % hold-out test method using R-Squared (R 2 ), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as the evaluation metrics. Among the five tree-based regression models Extra Trees Regressor performed better with highest R 2 values in the range of 0.99966–0.99974 and the lowest error metrics for all the emission parameters and demonstrates the outstanding robustness and generalization ability of the model. The stronger consistency of extra trees across different test samples was demonstrated by absolute error heatmaps, while the model's accuracy was further validated by comparing actual and predicted values. The study's overall findings demonstrate the potential of tree-based ensemble learning, and extra trees in particular, as a lightweight, accurate and reliable tool for real-time emission prediction in low-carbon dual-fuel systems. This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. Extra Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and AdaBoost are the tree-based ensemble regression models used to predict the emission parameters under limited data conditions. The performance of the models was assessed through 5-fold cross-validation and a 20 % hold-out test method using R-Squared (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as the evaluation metrics. Among the five tree-based regression models Extra Trees Regressor performed better with highest R2 values in the range of 0.99966–0.99974 and the lowest error metrics for all the emission parameters and demonstrates the outstanding robustness and generalization ability of the model. The stronger consistency of extra trees across different test samples was demonstrated by absolute error heatmaps, while the model's accuracy was further validated by comparing actual and predicted values. The study's overall findings demonstrate the potential of tree-based ensemble learning, and extra trees in particular, as a lightweight, accurate and reliable tool for real-time emission prediction in low-carbon dual-fuel systems. |
| ArticleNumber | 124726 |
| Author | S, Naveen Venkatesh Sugumaran, V. Renjit, E. Jeslin Subramanian, Thiyagarajan Varuvel, Edwin Geo Matijošius, Jonas Josephin J S, Femilda Kiani, Farzad Kilikevičius, Artūras Subramanian, Balaji |
| Author_xml | – sequence: 1 givenname: Femilda orcidid: 0000-0003-0249-9506 surname: Josephin J S fullname: Josephin J S, Femilda email: femilda.bai@istinye.edu.tr organization: Department of Computer Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkiye – sequence: 2 givenname: Balaji surname: Subramanian fullname: Subramanian, Balaji organization: Department of Mechanical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India – sequence: 3 givenname: E. Jeslin surname: Renjit fullname: Renjit, E. Jeslin organization: Department of Computer Application, BS Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India – sequence: 4 givenname: Naveen Venkatesh surname: S fullname: S, Naveen Venkatesh organization: School of Mechanical Engineering (SMEC), Vellore Institute of Technology - Chennai Campus, Chennai, India – sequence: 5 givenname: V. surname: Sugumaran fullname: Sugumaran, V. organization: School of Mechanical Engineering (SMEC), Vellore Institute of Technology - Chennai Campus, Chennai, India – sequence: 6 givenname: Thiyagarajan surname: Subramanian fullname: Subramanian, Thiyagarajan organization: Department of Mechanical Engineering, Alliance School of Applied Engineering, Alliance University, Bengaluru, 562106, Karnataka, India – sequence: 7 givenname: Farzad surname: Kiani fullname: Kiani, Farzad organization: Data Science Application and Research Center (VEBIM), Fatih Sultan Mehmet Vakif University, Istanbul, Türkiye – sequence: 8 givenname: Edwin Geo surname: Varuvel fullname: Varuvel, Edwin Geo organization: Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkiye – sequence: 9 givenname: Jonas surname: Matijošius fullname: Matijošius, Jonas organization: Institute of Mechanical Science, Vilnius Gediminas Technical University, Plytines Str. 25, 10105, Vilnius, Lithuania – sequence: 10 givenname: Artūras surname: Kilikevičius fullname: Kilikevičius, Artūras organization: Institute of Mechanical Science, Vilnius Gediminas Technical University, Plytines Str. 25, 10105, Vilnius, Lithuania |
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| Keywords | Dual fuel engine Ensemble learning algorithms Machine learning algorithm Emission prediction Alternative fuels |
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| SubjectTerms | Alternative fuels Drift och underhållsteknik Dual fuel engine Emission prediction Ensemble learning algorithms Machine learning algorithm Operation and Maintenance Engineering |
| Title | Tree-based ensemble regression models for emission prediction of a winter green oil-hydrogen dual-fuel engine with zeolite after-treatment |
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