<scp>ANN</scp> ‐ <scp>TOPSIS</scp> approach and non‐transition nanocatalyst influence on <scp>CI</scp> engine characteristics fueled with surfactants emulsified biodiesel blends

Abstract This study examines the performance and emission characteristics of a four‐stroke compression ignition engine fueled with STBD25, a blend comprising 25% Sapindus trifoliatus biodiesel and 75% diesel. The fuel was emulsified using magnesium‐doped calcium oxide, Span 80, and Tween 80. The research evaluated diesel and five biodiesel blends, focusing on TN80 concentrations of 15, 30, and 45 ppm, with SP80–TN80 ratios of 1:0.5, 1:1, and 1:1.5. The introduction of SP80 and TN80 improved several critical physicochemical properties, including the cetane number and heating value. However, changes in density, flash point, and viscosity were minimal. Among the tested blends, STBD25 emulsified with 30 ppm MDC and an SP80–TN80 ratio of 1:1 demonstrated the best performance. This blend achieved higher in‐cylinder pressure (7.11%–12.6%), heat release rate (6.09%–35.52%), and brake thermal efficiency (10.05%–16.01%). Additionally, it significantly reduced emissions of hydrocarbons (27.77%–38.8%), carbon monoxide (33.33%–66.66%), oxides of nitrogen (2.41%–18.02%), and smoke (20.12%–28.8%). An artificial neural network (ANN) was utilized to model engine characteristics under various load conditions, yielding a high correlation coefficient of 0.99956, which indicates excellent agreement with experimental results. The TOPSIS method was applied to identify optimal input conditions, resulting in higher brake thermal efficiency and reduced exhaust emissions. Under full load conditions, the blend of STBD25, 30 ppm SP80, 30 ppm TN80, and 30 ppm MDC was the most effective combination, achieving a relative closeness value of 0.999, signifying its superior performance.