Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel
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 meth...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6099019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850169722238140416 |
|---|---|
| author | Yue Wang Hamid Heydari |
| author_facet | Yue Wang Hamid Heydari |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-a9de4612216f4ae8b911ab8588f383f9 |
| institution | OA Journals |
| issn | 1687-806X 1687-8078 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-a9de4612216f4ae8b911ab8588f383f92025-08-20T02:20:40ZengWileyInternational Journal of Chemical Engineering1687-806X1687-80782021-01-01202110.1155/2021/60990196099019Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of BiodieselYue Wang0Hamid Heydari1School of Intelligent Manufacturing, Xinxiang Vocational and Technical College, Xinxiang, Henan 453003, ChinaInstitute of Petroleum Engineering, University of Tehran, Tehran, IranNowadays, 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%.http://dx.doi.org/10.1155/2021/6099019 |
| spellingShingle | Yue Wang Hamid Heydari Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel International Journal of Chemical Engineering |
| title | Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel |
| title_full | Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel |
| title_fullStr | Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel |
| title_full_unstemmed | Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel |
| title_short | Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel |
| title_sort | developing an extreme learning machine based model for estimating the isothermal compressibility of biodiesel |
| url | http://dx.doi.org/10.1155/2021/6099019 |
| work_keys_str_mv | AT yuewang developinganextremelearningmachinebasedmodelforestimatingtheisothermalcompressibilityofbiodiesel AT hamidheydari developinganextremelearningmachinebasedmodelforestimatingtheisothermalcompressibilityofbiodiesel |