Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mousavi, Seyed Pezhman, Atashrouz, Saeid, Nait Amar, Menad, Hemmati-Sarapardeh, Abdolhossein, Mohaddespour, Ahmad, Mosavi, Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783634351021359104
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
work_keys_str_mv AT mousaviseyedpezhman viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem
AT atashrouzsaeid viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem
AT naitamarmenad viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem
AT hemmatisarapardehabdolhossein viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem
AT mohaddespourahmad viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem
AT mosaviamir viscosityofionicliquidsapplicationoftheeyringstheoryandacommitteemachineintelligentsystem