Estimation of vegetable oil-based Ethyl esters biodiesel densities using artificial neural networks

In this study a new approach based on Artificial Neural Networks (ANNs) has been designed to predict the density of various vegetable oil-based ethyl esters biodiesel. The experimental densities data measured at various temperatures from 15 to 90°C at 1 °C interval were used to train the networks. The present work, applied a three layer back propagation neural network with nine neurons in the hidden layer. The results from the network are in good agreement with the measured data and the average absolute percent deviation are 0.35, 0.72, 0.54, 0.68 and 0.72% for the ethyl esters of palm, canola, corn and ricebran oil, respectively. The results of ANNs have also been compared with the results of theoretical estimations.