Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificial intelligence method of ANN-GA-RSM

The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to model performance and emissions a single cylinder diesel engine fueled by propylene glycol additive. evaluations are performed using correlation coefficient (CC), root mean square error (RMSE) values. best for prediction dependent variables is reported ANN-GA with RMSE values 0.0398, 0.0368, 0.0529, 0.0354, 0.0509 0.0409 CC 0.988, 0.987, 0.977, 0.994, 0.984, 0.990, respectively brake specific fuel consumption (BSFC), thermal efficiency (BTE), CO, CO2, NOx SO2. proposed reduces BSFC, NOx, CO −30.82%, 21.32%, 11.32%, respectively. also increases CO2 emission 17.29% 31.05%, respectively, compared RSM in optimized level independent (69% biodiesel's oxygen content 32% glycol).