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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Bibliographic Details
Published in:Renewable energy Vol. 257; p. 124726
Main Authors: Josephin J S, Femilda, Subramanian, Balaji, Renjit, E. Jeslin, S, Naveen Venkatesh, Sugumaran, V., Subramanian, Thiyagarajan, Kiani, Farzad, Varuvel, Edwin Geo, Matijošius, Jonas, Kilikevičius, Artūras
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2026
Subjects:
Alternative fuels
Drift och underhållsteknik
Dual fuel engine
Emission prediction
Ensemble learning algorithms
Machine learning algorithm
Operation and Maintenance Engineering
Dual fuel engine
Ensemble learning algorithms
Machine learning algorithm
Emission prediction
Alternative fuels
ISSN:0960-1481, 1879-0682
Online Access:Get full text
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Summary: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.
ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2025.124726