An accurate model for the prediction of the glass transition temperature of ammonium based ionic liquids: A QSPR approach

Mirkhani, Seyyed Alireza; Gharagheizi, Farhad; Ilani-Kashkouli, Poorandokht; Farahani, Nasrin

doi:10.1016/j.fluid.2012.03.024

Cited by 26 publications

(12 citation statements)

References 37 publications

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“…Some QSPR-based models for predicting the Tg of ILs have been reported in the literature. Many of them were developed for predicting the glass transition temperature of one family of ILs such as ammonium [39] or 1,3-dialkylimidazolium [40,41]. In these cases, the Tg is calculated as the sum of the contribution of the anion and the cation, and their corresponding descriptors are included in T g,a and T g,c terms:…”

Section: Qspr Methodsmentioning

confidence: 99%

Thermal Behaviour of Pure Ionic Liquids

Gómez¹,

Calvar²,

Domínguez³

2015

Ionic Liquids - Current State of the Art

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Section: Qspr Methodsmentioning

confidence: 99%

Thermal Behaviour of Pure Ionic Liquids

Gómez¹,

Calvar²,

Domínguez³

2015

Ionic Liquids - Current State of the Art

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“…He reported an average absolute relative deviation (AARD) of 5% between predicted and experimental data for 250 ionic liquids. Better model performances (AARD of 1.98% for 73 ILs, 1.38% for 109 ILsand2.68% for 109 ILs) were reported by Mirkhani et al [25], Mousavisafavi et al [26] and Mousavisafavi et al [27] respectively.…”

Section: Introductionmentioning

confidence: 72%

An enhanced group-interaction contribution method for the prediction of glass transition temperature of ionic liquids

Mokadem

Korichi

Tumba

2016

Fluid Phase Equilibria

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“…The GEP computational steps described in the previous works has been followed here to develop the model. The corresponding states principle parameters which comprise critical temperature ( T c ), critical pressure ( P c ), critical volume ( V c ), acentric factor (ω), normal boiling temperature ( T b ), temperature ( T ), and the molecular weight ( M w ) have been introduced as input parameters into the algorithm.…”

Section: Developing the Modelmentioning

confidence: 99%

“…As a result, in this study, we apply the Gene Expression Programming (GEP) algorithm to develop a general model for representation/prediction of the liquid thermal conductivities of more than 1600 liquids (mostly nonelectrolyte organic) at different temperatures but atmospheric pressure for temperatures below the normal boiling point and at saturation pressure for temperatures above the normal boiling point. The details of the GEP technique can be found elsewhere …”

Section: Introductionmentioning

confidence: 99%

Development of a general model for determination of thermal conductivity of liquid chemical compounds at atmospheric pressure

et al. 2012

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In this communication, a general model for representation/presentation of the liquid thermal conductivity of chemical compounds (mostly organic) at 1 atm pressure for temperatures below normal boiling point and at saturation pressure for temperatures above the normal boiling point is developed using the Gene Expression Programming algorithm. Approximately 19,000 liquid thermal conductivity data at different temperatures related to 1636 chemical compounds collected from the DIPPR 801 database are used to obtain the model as well as to assess its predictive capability. The parameters of the model comprise temperature, acentric factor, critical pressure, normal boiling temperature, and molecular weight. Nearly 80% of the data set (15,221 data) is randomly assigned to develop the model equation, 10% of the data set (1902 data) is used to validate the model, and the remaining data (1902 data) were implemented to evaluate its predictive power. The average absolute relative deviation of the model results from the DIPPR 801 data is less than 9%. In terms of simplicity and wide range of applicability, this empirical model shows acceptable accuracy. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1702–1708, 2013

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An accurate model for the prediction of the glass transition temperature of ammonium based ionic liquids: A QSPR approach

Cited by 26 publications

References 37 publications

Thermal Behaviour of Pure Ionic Liquids

Thermal Behaviour of Pure Ionic Liquids

An enhanced group-interaction contribution method for the prediction of glass transition temperature of ionic liquids

Development of a general model for determination of thermal conductivity of liquid chemical compounds at atmospheric pressure

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