Prediction of biodiesel fuel properties from its fatty acids composition using ANFIS approach

Biodiesel is renewable fuel, environment-friendly and a potential substitute for petroleum diesel. The biodiesel properties are based on the type of used oil and its structure. The aim of this study is to model and predict biodiesel properties such as kinematic viscosity, iodine value, cloud point and pour point from fatty acids composition using ANFIS approach. The input variables were carbon number (Cn), the number of double bonds (dn), wt% of mono unsaturated fatty acids (MU), wt% of poly unsaturated fatty acids (PU), wt% of saturated fatty acids C0, temperature (T), and molar weight (Mw). The performance of developed ANFIS model was compared using statistical criteria such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute percent error (MAPE). It was determined that the coefficient of determination, R2 related to kV, IV, CP, and PP were 0.989, 0.996, 0.938, and 0.981 respectively. The RMSE and MAPE criteria were ranges between 0.28 and 2.15 and 0.25-0.62 in the order already mentioned. Consequently, the results show that developed ANFIS models have a higher accuracy and predictive ability.