Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel

Abstract

Nowadays, the high consumption of fossil fuels has caused many pollutants and environmental problems. Biodiesel has recently been considered as a clean and renewable alternative to fossil fuels. They are found in some molecular structures including fatty acid ethyl esters (FAEEs) and also fatty methyl esters (FAMEs), having various thermophysical characteristics. Thus, it appears essential to select the suitable methods for a particular diesel engine to estimate the ester characteristics. The current research sets out to develop a new and robust method predicting isothermal compressibility of long-chain fatty acid methyl and ethyl esters directly from several basic efficient parameters (pressure, temperature, normal melting point, and molecular weight). Therefore, as a novel and prevailing mathematical method in this field, an extreme learning machine was implemented for isothermal compressibility on the massive dataset. According to statistical evaluations, this novel established model had high accuracy and applicability (R2 = 1 and RMSE = 0.0018714) which is more accurate than previous models presented by former researchers. Among various factors of the sensitivity analysis, temperature and pressure had the greatest effect on the output values, so that the output parameter has a direct relationship with temperature and an inverse relationship with pressure with relevancy factors of 22.44% and −79.81%.