Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine DOI Creative Commons
Komarova Le, Minh Thai Duong, Dao Nam Cao

et al.

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(6), P. 1175 - 1190

Published: Oct. 27, 2024

In the ongoing search for an alternative fuel diesel engines, biogas is attractive option. Biogas can be used in dual-fuel mode with as pilot fuel. This work investigates modeling of injecting strategies a waste-derived biogas-powered engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), Absolute Percentage (MAPE) among criteria evaluations models. Random Forest has shown better Brake Thermal Efficiency (BTE) test R² 0.9938 low MAPE 3.0741%. once more exceeded other models 0.9715 4.2242% estimating Specific Energy Consumption (BSEC). With 0.9821 2.5801% emerged most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results monoxide (CO) prediction based on obtained 0.8339 3.6099%. outperformed Linear Regression 0.9756% 7.2056% case nitrogen oxide (NOx) emissions. showed constant overall criteria. paper emphasizes how well especially prognosticate engines.

Language: Английский

Timely achievement of carbon peak for China: evidence from major energy-consuming industries DOI
Haize Pan, Chuan Liu, Jian He

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 30, 2024

Language: Английский

Citations

0
Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine DOI Creative Commons
Komarova Le, Minh Thai Duong, Dao Nam Cao

et al.

International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(6), P. 1175 - 1190

Published: Oct. 27, 2024

In the ongoing search for an alternative fuel diesel engines, biogas is attractive option. Biogas can be used in dual-fuel mode with as pilot fuel. This work investigates modeling of injecting strategies a waste-derived biogas-powered engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), Absolute Percentage (MAPE) among criteria evaluations models. Random Forest has shown better Brake Thermal Efficiency (BTE) test R² 0.9938 low MAPE 3.0741%. once more exceeded other models 0.9715 4.2242% estimating Specific Energy Consumption (BSEC). With 0.9821 2.5801% emerged most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results monoxide (CO) prediction based on obtained 0.8339 3.6099%. outperformed Linear Regression 0.9756% 7.2056% case nitrogen oxide (NOx) emissions. showed constant overall criteria. paper emphasizes how well especially prognosticate engines.

Language: Английский

Citations

0