Prediction of the water content of biodiesel using ANN-MLP: An environmental application

Abstract

ABSTRACTFuel quality, especially biodiesel, is highly dependent on its water content, and the major sources of water in the fuel relate to the transportation, production, and storage processes. In this present contribution, the multilayer perceptron artificial neural network (MLP-ANN) was applied to predict the water content of biodiesel and diesel blend in terms of temperature and composition. The proposed algorithm was trained and tested by utilizing 400 experimental data points which were extracted from the literature. Based on the results, the MLP-ANN model has great ability to estimate the water content of biodiesel and diesel blend. The R-squared (R2), root mean square error, average absolute relative deviation, and a​bsolute deviation parameters for the total data set are obtained, respectively, as 0.99784, 123919.1172, 3.3632, and 1.17%, which indicate the effective performance suggested by ANN. As the computational study is cheaper and easier than the experimental study, the developed software could be considered as an alternative for laboratory study, and the environmental effect of biodiesel and produced undesired product after biodiesel combustion which is directly related to the water content of biodiesel is estimable with the information released in this study.