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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Vydáno v:Fuel (Guildford) Ročník 357; s. 129914
Hlavní autoři: Lionus Leo, G.M., Jayabal, Ravikumar, Srinivasan, D., Chrispin Das, M., Ganesh, M., Gavaskar, Thodda
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2024
Témata:
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
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Shrnutí:•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.•The random forest machine learning algorithm is used to predict engine outcomes. This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50–50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often assuming linear correlations or disregarding variable interdependencies.
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2023.129914