Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection – A random forest machine learning approach

•Energy utilization from waste cooking oil is extensively studied.•Al2O3 and FeCl3 were used as an ignition enhancer to reduce emissions.•Smoke, HC and CO emissions decreased by doping of Al2O3 and FeCl3 nano additive.•HCCI-DI combustion decreased NOx emissions by 4.3 % compared to conventional DI.•...

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Veröffentlicht in:	Fuel (Guildford) Jg. 357; S. 129914
Hauptverfasser:	Lionus Leo, G.M., Jayabal, Ravikumar, Srinivasan, D., Chrispin Das, M., Ganesh, M., Gavaskar, Thodda
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2024
Schlagworte:	Aluminum oxide nano additive Emissions Ferric chloride nano additive Random forest algorithm Waste cooking oil biodiesel HCCI FeCl3 DI CO2 MAPE CO Aluminum oxide nano additive Al2O3 Emissions BTE Random forest algorithm HCCI-DI NOx NRSME WCOB Waste cooking oil biodiesel HC Ferric chloride nano additive MSE RSME
ISSN:	0016-2361, 1873-7153
Online-Zugang:	Volltext
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