Improved estimation of Cetane number of fatty acid methyl esters (FAMEs) based biodiesels using TLBO-NN and PSO-NN models

Abstract

Cetane number (CN) is one of the key factors of biodiesels and other diesel fuels. It is an indicator of ignition speed and required compression for ignition. CN estimation of biodiesel based on fatty acid methyl esters (FAME) composition was the main goal of this work. Application of artificial neural network (ANN) combined with particle swarm optimization (PSO) and teaching-learning based optimization (TLBO) is discussed in this communication. A number of 232 fuel samples was derived from the literature as the raw data for the models development. Different evaluative factors prove the satisfactory performance of the proposed ANN models. The obtained values of R-squared and mean square of errors are 0.973 & 3.538 and 0.951 & 6.324 for the proposed TLBO-ANN and PSO-ANN, respectively. Based on the outcome of this study, ANN coupled with PSO and TLBO algorithms can be suitable tools, especially TLBO algorithm to estimate CN of biodiesels.