Artificial neural network based optimization of engine performance using domestic biomass as a clean fuel

Area of Science:

• Sustainable Energy
• Biomass Conversion
• Nanotechnology Applications

Background:

• Growing demand for sustainable energy drives research into renewable biomass.
• Poultry waste presents an underutilized resource for biofuel production.

Purpose of the Study:

• To investigate poultry waste as a feedstock for biodiesel generation.
• To optimize biodiesel production conditions and assess environmental impact.
• To evaluate the performance of Zinc sulfide (ZnS) nanofluid fuel blends.

Main Methods:

• Optimized biodiesel production from chicken waste using specific methanol-to-oil ratio, catalyst concentration, reaction time, and mixing speed.
• Prepared nanofluid formulations with Zinc sulfide (ZnS) nanopowder at varying concentrations (50-150 ppm).
• Blended nanofluids with a C20D80 fuel mixture and evaluated engine performance and emissions.
• Utilized AI-based neural networks for experimental data validation.

Main Results:

• Optimal biodiesel yield and lowest Environmental Factor (E-factor) achieved at a 9:1 methanol-to-chicken-oil ratio, 1% catalyst, 1-h reaction, and 500 rpm.
• The C20D80 + 100 ppm ZnS nanofluid blend significantly reduced HC (29.23%), CO (37%), and NOx (5.7%) emissions.
• AI models demonstrated high accuracy (R² 0.9197–0.9961) in substantiating experimental findings.
• Comparable engine performance to standard diesel was maintained with the nanofluid blend.

Conclusions:

• Poultry waste is a viable feedstock for sustainable biodiesel production.
• ZnS nanofluids enhance the emission reduction capabilities of biodiesel fuels.
• Integration of waste biomass, E-factor assessment, and nanotechnology offers a promising pathway for cleaner energy solutions.