Enhancement of compression ignition engine characteristics of waste transformer oil-derived biodiesel via an Ag 3 PO 4 /Ag/TiO 2 –carbon nanocomposite and performance prediction using an artificial neural network approach

The objective of this study is to enhance the compression ignition engine characteristics with a waste transformer oil biodiesel dosing Ag 3 PO 4 /Ag/TiO 2 –carbon nanocomposite as a fuel additive with 80% diesel, 20% waste transformer oil, and nanocomposites of 500, 1000, and 1500 ppm. This analysis revealed that up to 1000 ppm of the nanocomposite, the synergic effect improved the brake thermal efficiency, peak cylinder pressure, and peak heat release rate by 11.5%, 3.6%, and 4.5% than diesel at 100% load. It also reduced the brake-specific fuel consumption, carbon monoxide, unburned hydrocarbon, and smoke emissions by 9.5%, 18%, 18.3%, and 20% respectively, alongside increased nitrogen oxide emission by 26.6%. Further increasing the concentration of the nanocomposite leads to a decline in the characteristics of the engine. To determine the optimal blend, the numerical optimization has been done using the approach of artificial neural networks (ANNs). Furthermore, the ANN model exhibited high prediction accuracy with a correlation coefficient ( R ) close to unity (0.99–1.00), showing strong agreement between the experimental and predicted results, and effectively identified the optimal nanocomposite concentration of 1000 ppm. Additionally, the integration of experimental analysis with ANN-based optimization to identified 1000 ppm as the best-performing nanocomposite concentration among the investigated cases.