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

本研究提出了一种人工神经网络-遗传算法-响应面方法（ANN-GA-RSM）的混合机器学习算法，以模拟柴油和丙二醇添加剂为燃料的单缸柴油机的性能和排放。使用相关系数（CC）和均方根误差（RMSE）值进行评估。预测因变量的最佳模型是ANN-GA，其RMSE值分别为0.0398、0.0368、0.0529、0.0354、0.0509和0.0409，并且CC 0.988、0.987、0.977、0.994、0.984、0.990分别为CC （BSFC），刹车热效率（BTE），CO，CO2，NOx和SO2。提出的混合模型分别将BSFC，NOx和CO降低了30.82％，21.32％和11.32％。该模型还提高了发动机效率和CO₂发射分别17.29％和31.05％，相比于独立变量（生物柴油的氧含量为69％和丙二醇的氧含量的32％）的优化电平的单个RSM。