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 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.02.2024
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| ISSN: | 0016-2361, 1873-7153 |
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| Abstract | •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. |
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| AbstractList | •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. |
| ArticleNumber | 129914 |
| Author | Gavaskar, Thodda Chrispin Das, M. Srinivasan, D. Ganesh, M. Jayabal, Ravikumar Lionus Leo, G.M. |
| Author_xml | – sequence: 1 givenname: G.M. surname: Lionus Leo fullname: Lionus Leo, G.M. organization: Department of Mechanical Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India – sequence: 2 givenname: Ravikumar surname: Jayabal fullname: Jayabal, Ravikumar email: raviram03@gmail.com organization: Department of Thermal Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India – sequence: 3 givenname: D. surname: Srinivasan fullname: Srinivasan, D. organization: Department of Mechanical Engineering, Loyola Institute of Technology, Chennai, Tamil Nadu, India – sequence: 4 givenname: M. surname: Chrispin Das fullname: Chrispin Das, M. organization: Department of Mechanical Engineering, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India – sequence: 5 givenname: M. orcidid: 0000-0003-4517-1906 surname: Ganesh fullname: Ganesh, M. organization: Department of Mechanical Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India – sequence: 6 givenname: Thodda surname: Gavaskar fullname: Gavaskar, Thodda organization: Department of Mechanical Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India |
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| Keywords | 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 |
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| SubjectTerms | Aluminum oxide nano additive Emissions Ferric chloride nano additive Random forest algorithm Waste cooking oil biodiesel |
| Title | 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 |
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