
Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 105650 - 105650
Published: Dec. 1, 2024
Language: Английский
Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 105650 - 105650
Published: Dec. 1, 2024
Language: Английский
Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103196 - 103196
Published: Oct. 1, 2024
Language: Английский
Citations
16Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106810 - 106810
Published: Jan. 1, 2025
Language: Английский
Citations
0Energy Science & Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: April 10, 2025
ABSTRACT This study investigates the performance and emission characteristics of a diesel engine running on blend Scenedesmus obliquus (SO) biodiesel, pyrolyzed waste plastic oil (WP), n‐heptane (H), acetylene induction (SOPWH), tested under various loads at 1500 rpm. The goal is to enhance brake thermal efficiency (BTE) reduce emissions in alignment with Sustainable Development Goals (SDGs) 7 11, which focus clean energy sustainable cities. SO biodiesel was produced via transesterification algae grown recycled wastewater, while WP obtained through pyrolysis 450°C. n‐Heptane added optimize blend, 4 liters per minute inducted into intake manifold. Results showed that SO5WP5H10 improved BTE by 0.93% no load reduced brake‐specific consumption (BSEC) 0.686 MJ/kW‐h part load, compared diesel. Emission reductions included 28% decrease hydrocarbon (HC) 35% reduction carbon monoxide (CO) full SO20WP20H10 blend. Smoke opacity decreased 19% maximum load. In terms combustion, achieved peak in‐cylinder pressure 77.5 bar an efficient mass fraction burnt (MFB), reaching 90% 25 o CA after TDC. Overall, SOPWH blends enhanced combustion cleaner emissions, positioning them as alternative diesel, without need for modifications. aligns global sustainability goals offering cleaner, more fuel option engines.
Language: Английский
Citations
0International Journal of Renewable Energy Development, Journal Year: 2024, Volume and Issue: 13(4), P. 783 - 813
Published: June 7, 2024
This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on energy domains solar, wind, biofuel, biomass. Critical problems such as data variability, system inefficiencies, predictive maintenance are addressed by integration ML in systems. Machine improves solar irradiance prediction accuracy maximizes photovoltaic performance sector. help to generate electricity more reliably enhancing wind speed forecasts turbine efficiency. efficiency biofuel production optimizing feedstock selection, process parameters, yield forecasts. Similarly, models biomass provide effective thermal conversion procedures real-time management, guaranteeing increased operational stability. Even enormous advantages, quality, interpretability models, computing requirements, current systems still remain. Resolving these issues calls interdisciplinary cooperation, developments computer technology, encouraging legislative frameworks. study emphasizes vital role promoting sustainable efficient giving a thorough present applications highlighting continuing problems, outlining future prospects
Language: Английский
Citations
3Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 105650 - 105650
Published: Dec. 1, 2024
Language: Английский
Citations
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