محبوبه شیرانی

Two potent mathematical and statistical methods of response surface methodology (RSM) and genetic algorithm (GA) based on artificial neural network (ANN) were employed for prediction and optimization of three-constituent synthetic engine oil. Polyalpha olephin-4 (PAO4) Hitec 5780 (HI 5780), and Hitec 11100 (HI 11100) were used as base oil and the additives of the engine oil, respectively. The models were applied for the percentage of oil constituents and viscosity at 40 ˚C (Vis at 40 ˚C), viscosity at 100 ˚C (Vis at 100 ˚C), Viscosity index (VI), flash point (FP) and Noack of the finished oil. The range of the viscosity at 40 ºC and 100 ºC were selected according to ISO viscosity grade for engine oil. The optimization includes maximization of FP and VI and minimization of Noack. The obtained results showed that ANN has higher potential and capability and more accuracy for prediction and optimization of the process