Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization

Energy Conversion and Management X, Год журнала: 2025, Номер unknown, С. 101000 - 101000

Опубликована: Апрель 1, 2025

Язык: Английский

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A critical review on conversion technology for liquid biofuel production from lignocellulosic biomass

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 217, С. 115726 - 115726

Опубликована: Апрель 14, 2025

Язык: Английский

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General models for predicting the liquid thermal conductivity of fatty acid esters based on smart methods

Energy Conversion and Management X, Год журнала: 2025, Номер unknown, С. 101023 - 101023

Опубликована: Апрель 1, 2025

Язык: Английский

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Performance and emissions of gas turbine engines fueled with karanja oil-based biofuel blends: a machine learning approach using Lasso regression

Aircraft Engineering and Aerospace Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 28, 2025

Purpose This study aims to investigate the performance and emission characteristics of gas turbine engines operating on biofuel blends derived from karanja oil as a potential alternative conventional Jet-A fuel. Design/methodology/approach The tested three blends: JA20 (20% oil, 80% Jet-A), JA30 (30% 70% Jet-A) JA40 (40% 60% diesel). Engine parameters, including thrust output, thrust-specific fuel consumption (TSFC) inlet temperature (TIT), were measured at engine speeds ranging 30,000 80,000 rpm. Exhaust emissions carbon monoxide (CO), dioxide (CO 2 ) nitrogen oxides (NOx) analyzed using analyzer. Additionally, Lasso regression model was used predict micro (MGT) based experimental data. Findings Increasing content in reduced overall increased TSFC lowered TIT compared blend exhibited most significant reduction, with 20% decrease 7.5% increase 4.1% TIT. However, consistently resulted lower CO, CO NOx fuel, reductions up 36%, 6.9% 13.6%, respectively, for blend. effectively captured influence speed composition emissions, achieving an R ² 0.95 0.94 predictions. Originality/value provides insights into feasibility oil-based biofuels engines, demonstrating their reduce while highlighting tradeoffs performance. use predicting offers novel approach assessing MGTs.

Язык: Английский

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Optimizing Green Urea Production: Integration of Process Simulation, Artificial Intelligence, and Sustainable Technologies

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 145371 - 145371

Опубликована: Март 1, 2025

Язык: Английский

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