A Study of Diesel Engine Performance and Emission using Artificial Neural Networks with Biodiesel from used Temple Oil

Abstract

A major threat is environmental pollution, particularly of gases resulting from the combustion of fossil fuels. Transesterification of used temple oil in methyl ester blended biodiesel at 25%, 50%, 75%, and 100% was studied. These were studied to see how they would affect engine emissions and performance. Results at 1500 rpm reduced brake power and thermal efficiency by 25% and 24%, respectively, compared to diesel. Indeed, however, the specific fuel consumption of diesel increased by 23%. According to Cam Bell’s analysis, methyl ester blends led to a 15% lower air-fuel ratio and 4% lower volumetric efficiency than diesel. B100 biodiesel showed significant emission reductions: carbon monoxide 12%, hydrocarbons 44%, and smoke 48%. Under full load, NOx emissions were increased by 200 percent. Engine performance and emission modeling were optimized using ANN, leading to a reduction in costs as well as physical effort and facilitating the handling of nonlinear datasets.