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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Renewable energy Jg. 257; S. 124726
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2026
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!