Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System.

CMIS modeling Eyring’s theory artificial intelligence artificial neural networks ionic liquids machine intelligent system machine learning viscosity

Journal

Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009

Informations de publication

Date de publication:
31 Dec 2020
Historique:
received: 27 10 2020
revised: 05 12 2020
accepted: 15 12 2020
entrez: 5 1 2021
pubmed: 6 1 2021
medline: 14 4 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 33396329
pii: molecules26010156
doi: 10.3390/molecules26010156
pmc: PMC7795042
pii:
doi:

Substances chimiques

Ionic Liquids 0
Solvents 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

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Auteurs

Seyed Pezhman Mousavi (SP)

Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran.

Saeid Atashrouz (S)

Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez 424, Tehran 15875-4413, Iran.

Menad Nait Amar (M)

Département Etudes Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes 35000, Algeria.

Abdolhossein Hemmati-Sarapardeh (A)

Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran.
College of Construction Engineering, Jilin University, Changchun 130600, China.

Ahmad Mohaddespour (A)

College of Engineering and Technology, American University of the Middle East, Dasman, Kuwait.

Amir Mosavi (A)

Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany.
School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway.
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.

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Classifications MeSH