Abstract The present study investigates the performance, combustion and emission characteristics of ternary blends of diesel, pentanol and ethanol through combined experimental and computational approaches. Pentanol is selected as a co-solvent for its unique chemical compatibility and ability to stabilize ethanol-diesel blends, thus preventing phase separation and enhancing blend performance. Experimental tests were conducted at a fixed engine speed (1500 rpm), compression ratio (17.5:1) and injection timing (23° bTDC) to evaluate the influence of blend composition under varying loads. The results show that, compared to neat diesel, the ternary blends yield increases in brake thermal efficiency (0.27-3.38%) and NOx emissions (5.56-32.01%), while exhaust gas temperature and smoke opacity decrease (0.73-6.1% and 4.59-22.57%, respectively). Artificial neural network (ANN) modeling, using a 4-16-4 architecture and Levenberg-Marquardt training, accurately predicts engine responses. The fuzzy logic system, applied to experimental and ANN data, identifies 85% diesel, 9% pentanol and 6% ethanol at 40% load as the optimal blend for performance-emission trade-off. This integrated approach demonstrates the effectiveness of artificial intelligence (AI)-driven optimization for advancing sustainable alternative fuels.
Sinha et al. (Fri,) studied this question.