Experimental and Machine Learning Analysis of a CI Engine Fueled with American Saffron Biodiesel–Diesel Blends

Abstract

Alternative fuels come from non-traditional sources and can partly replace fossil fuels to cut environmental impact and support sustainable energy use. A single-cylinder diesel engine with rated power 5.2 Kw at 1500 rpm was tested with American Saffron Biodiesel blends. B0 (diesel), B10 (B10D90), B20 (B20D80), and B30 (B30D70), under different loads, and the results were compared with diesel. B20 showed the best improvement, with 13.41% higher brake thermal efficiency and 30.3% lower fuel usage than conventional fuel, at 100% load. B30 reduced HC by 5.88%, while B10 gave the lowest CO, reduced by 8.51%. The main contribution is the use of decision tree machine-learning regression to predict and optimize performance and emissions. The model achieved R = 0.91 and R² = 0.83, supporting the prediction of multivariable engine responses across operating conditions.