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Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System
Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method bas...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795042/ https://www.ncbi.nlm.nih.gov/pubmed/33396329 http://dx.doi.org/10.3390/molecules26010156 |
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author | Mousavi, Seyed Pezhman Atashrouz, Saeid Nait Amar, Menad Hemmati-Sarapardeh, Abdolhossein Mohaddespour, Ahmad Mosavi, Amir |
author_facet | Mousavi, Seyed Pezhman Atashrouz, Saeid Nait Amar, Menad Hemmati-Sarapardeh, Abdolhossein Mohaddespour, Ahmad Mosavi, Amir |
author_sort | Mousavi, Seyed Pezhman |
collection | PubMed |
description | Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement. |
format | Online Article Text |
id | pubmed-7795042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77950422021-01-10 Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System Mousavi, Seyed Pezhman Atashrouz, Saeid Nait Amar, Menad Hemmati-Sarapardeh, Abdolhossein Mohaddespour, Ahmad Mosavi, Amir Molecules Article Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement. MDPI 2020-12-31 /pmc/articles/PMC7795042/ /pubmed/33396329 http://dx.doi.org/10.3390/molecules26010156 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mousavi, Seyed Pezhman Atashrouz, Saeid Nait Amar, Menad Hemmati-Sarapardeh, Abdolhossein Mohaddespour, Ahmad Mosavi, Amir Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title | Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title_full | Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title_fullStr | Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title_full_unstemmed | Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title_short | Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System |
title_sort | viscosity of ionic liquids: application of the eyring’s theory and a committee machine intelligent system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795042/ https://www.ncbi.nlm.nih.gov/pubmed/33396329 http://dx.doi.org/10.3390/molecules26010156 |
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