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Processes
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Ionic Liquids: Solvent Properties and Organic Reactivity
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Ionic Liquids: Solvent Properties and Organic Reactivity

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1461

Special Issue Editors

Dr. Elías Fierro Antipi

E-Mail Website
Guest Editor
Departamento de Física, Universidad de Concepción, Casilla 160-C, Concepción 3349001, Chile
Interests: thermodynamical models; machine learning method; vapor–liquid equilibria; ionic liquid properties; EoS models
Dr. Ariana Muñoz Espinoza

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Chile, 5 Poniente 1670, Talca 3480094, Chile
Interests: thermodynamical models; machine learning method; vapor–liquid equilibria; ionic liquid properties; EoS models

Special Issue Information

Dear Colleagues,

Ionic Liquids (IL) are a new class of solvents which are characterized as a "green" alternative in separation processes. The study of these green solvents has increased in recent years due to the increasing number of potential ionic liquids. Among their properties are their low vapor pressure, melting temperature, ability to dissolve organic and inorganic materials, ability to dissolve polymeric inorganic materials, and high thermal and chemical stability. Since it is not possible to make every combination of ions and measure their properties, different methods are currently used to study them, such as experimental measurements, thermodynamic models, and computational methods.

This Special Issue on “Ionic Liquids: Solvent Properties and Organic Reactivity” aims to curate novel advances in the development and application of methods and technologies for the study of the physico-chemical properties of ionic liquids that are relevant to determine the capacity ionic liquids to affect some organic reactions. Topics include, but not are limited to:

  • The development and application of thermodynamic models for the study of the properties of ionic liquids and mixtures composed of ionic liquids.
  • The application of computational models for the study of the properties of ionic liquids and mixtures composed of ionic liquids.
  • Experimental studies of the properties of ionic liquids and mixtures composed of ionic liquids.

I hope you consider participating in this Special Issue.

Sincerely,

Dr. Elías Fierro Antipi
Dr. Ariana Muñoz Espinoza
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

15 pages, 3126 KiB  
Article
Solubility of Methane in Ionic Liquids for Gas Removal Processes Using a Single Multilayer Perceptron Model
by Claudio A. Faúndez, Elías N. Fierro and Ariana S. Muñoz
Processes 2024, 12(3), 539; https://doi.org/10.3390/pr12030539 - 08 Mar 2024
Viewed by 499
Abstract
In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility [...] Read more.
In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%. Full article
(This article belongs to the Special Issue Ionic Liquids: Solvent Properties and Organic Reactivity)
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16 pages, 1026 KiB  
Article
Use of Thermodynamically Consistent Phase Equilibrium Data to Obtain a Generalized Padé-Type Model for the Henry’s Constants of Gases in Ionic Liquids
by Claudio A. Faúndez, Luis A. Forero and José O. Valderrama
Processes 2024, 12(2), 343; https://doi.org/10.3390/pr12020343 - 06 Feb 2024
Viewed by 581
Abstract
A generalized Padé-type expression is proposed for Henry’s constant of gases in ionic liquids. The constants are determined using an equation of state, and generalized expressions for the Henry’s constants of the gases in the ionic liquids are proposed. The solute gases included [...] Read more.
A generalized Padé-type expression is proposed for Henry’s constant of gases in ionic liquids. The constants are determined using an equation of state, and generalized expressions for the Henry’s constants of the gases in the ionic liquids are proposed. The solute gases included in the study were oxygen, hydrogen, and carbon monoxide in three solvent ionic liquids ([MDEA][Cl], [Bmim][PF6], and [Hmim][TF2N]). The Valderrama–Patel–Teja equation of state with the mixing rules of Kwak and Mansoori are employed to correlate the solubility data, to examine the thermodynamic consistency of the experimental data, and to determine the fugacity (fi) for each concentration (xi) of the solute gas in the liquid phase. From these data, the fugacity coefficients (fiL/xi) are determined to obtain Henry´s constant as Hi = lim(fiL/xi) when xi→0. The calculated Henry’s constants are correlated in terms of the temperature and acentric factor of the gases to finally obtain a generalized expression for Henry´s constant, Hi. Full article
(This article belongs to the Special Issue Ionic Liquids: Solvent Properties and Organic Reactivity)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Solubility of methane in ionic liquids for gas removal processes using a single Multilayer Perceptron model
 
Abstract
 
In this work, four hundred and forty experimental solubility data of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight learning algorithms are tested to determine the appropriate model and architectures composed of one input layer, two hidden layers and one output layer are analyzed. The input variables of the artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility factor (Zc). The findings show that the (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables is a simple 45-parameter model with absolute deviation less than 12% in prediction.
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