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
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.02.2026
Predmet:	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
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