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

Yue Wang,Hamid Heydari,Alireza Baghban

doi:10.1155/2021/6099019

Yue Wang, Hamid Heydari + Show 1 more

Open Access

https://doi.org/10.1155/2021/6099019

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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%.

Highlights

Conventional hydrocarbon fuels have limited resources and lead to environmental pollution, asphaltene deposition, and formation damage. e rise in the use and contamination of fossil fuels in 2008 by up to 2500 million tons draws the attention of researchers to the quest for better substitutes for common fuels
Biodiesel is composed of long-chain fatty methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs), generated via transesterification of industrial and natural fatty acid resources, such as vegetable oil and animal fat, besides other fatty feedstock. e long-chain fatty acids’
Where R and R′ are the long-alkyl and short-alkyl chains, respectively. This reaction outlet, in a reactor unit, is a mixture of different kinds of long-chain FAMEs and FAEEs, typically related to impurities. e mixture is well-known as biodiesel

Summary

Introduction

Conventional hydrocarbon fuels have limited resources and lead to environmental pollution, asphaltene deposition, and formation damage. e rise in the use and contamination of fossil fuels in 2008 by up to 2500 million tons draws the attention of researchers to the quest for better substitutes for common fuels. Biodiesel is composed of long-chain FAMEs and FAEEs, generated via transesterification of industrial and natural fatty acid resources, such as vegetable oil and animal fat, besides other fatty feedstock. E characteristics studied (density, sound speed, isentropic compressibility, and isothermal compressibility) are the roles of fatty acid esters’ pressure, temperature, and molecular structure, which are the major biodiesel components. Several approaches, including the ANFIS and ANN, have been used to investigate and model biodiesel density at a variety of pressures and temperatures [24,25,26,27,28,29]. The current research aims at proposing a new and appropriate model to predict isothermal compressibility of several long-chain fatty acid esters based on modest and basic parameters, such as pressure, temperature, normal melting point, and molecular weight. Some other benefits of the models are their simplicity, and basic parameters, the high precision

Materials and Methods

MRE n

Data index

Current work

Leverage limit Standard residual limit