Artificial neural network modeling and experimental analysis of marine microalgae biodiesel blended with ZnO nanoparticles in a CI engine

This study investigates the effects of marine microalgae biodiesel blended with ZnO nanoparticles on diesel engine performance characteristics. Marine microalgae oil was extracted using the Soxhlet method with petroleum ether as the solvent, and biodiesel was prepared via a base-catalyzed transesterification process. A 25% biodiesel blend (A25) was prepared, and ZnO nanoparticles were added at concentrations of 50 ppm and 100 ppm. Engine tests conducted under varying load conditions were used to evaluate the brake thermal efficiency (BTE), specific fuel consumption (SFC), cylinder pressure, heat release rate, and exhaust emissions (CO, CO2, HC, NOx, and smoke). The test results revealed that A25 with 100 ppm ZnO nanoadditive achieved a 7.35% higher BTE, a 7.69% reduction in SFC, and a 2.07% increase in cylinder pressure. CO and HC emissions decreased by 54.1% and 10.30%, respectively, whereas NOx and smoke emissions were reduced by up–2.63% and 4.64% compared with those of A25. The Artificial Neural Network model effectively predicted the engine and emission behaviours with R-values above 0.99 and low MAPE/RMSE, validating its reliability. Overall, the results demonstrate the potential of ZnO-enhanced marine microalgae biodiesel as a sustainable, efficient, and eco-friendly fuel for CI engine applications.