APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS AND ARTIFICIAL NEURAL NETWORK AND FOR KINEMATIC VISCOSITY OF BIODIESEL PREDICTION

This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the fatty acid methyl esters (FAMEs) property including kinematic viscosity at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity of pure biodiesel was used for developing of models, where the input variables in the network were the temperature, the number of carbon atoms (NC) and the number of hydrogen atoms (NH) of the composition of methyl esters (C8:0, C10:0, C12:0, C14:0, C16:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:0, C20:1, C22:0, C22:1, C24:0 were considered as input variables on the ANFIS and ANN. Moreover, the models are divided into saturated species from C8:0 to C24:0 and unsaturated species, from C16:1 to C22:1. The model results were compared with experimental ones for determining the accuracy of the ANFIS and ANN predictions. The developed model produced idealized results and was found to be useful for predicting the kinematic viscosity of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity of biodiesel blends at various volume fractions and temperature compared to ANN approach.