Prediction of CO2 solubility in ionic liquids with QM and UNIQUAC models

Kamgar, Amin; Rahimpour, Mohammad Reza

doi:10.1016/j.molliq.2016.06.107

Cited by 17 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When designing ILs for carbon capture using CAMD, 8 it is important to develop a reliable model for predicting the solubility of CO 2 in ILs. Many traditional thermodynamic or physical models, for example, PHSC state equation, Gibbs–Duhem function, cubic equation of state combined with UNIFAC, COSMO‐RS, and DFT could estimate gas solubility 9–16 . These models could well represent the effects of temperature and pressure, because their thermodynamic models had excellent prediction performance, but sometimes these models could not give satisfactory prediction results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

et al. 2023

View full text Add to dashboard Cite

In this study, novel molecular structure encoding descriptors composed of feature encoding and one‐hot encoding was developed and then convolutional autoencoder was used to denoise based on the structure of ionic liquids (ILs). It could be used to predict the CO2 solubility in ILs at different temperatures and pressures, when combined with three different machine learning algorithms (multilayer perceptron [MLP], random forest [RF], and support vector machine [SVM]). Statistics of the prediction results show that the newly proposed molecular structure‐based coding has better regression prediction performance than the conventional molecular cheminformatics descriptors. SE‐MLP model with R2 of 0.9873 and mean square error of 0.0007 has the best performance in predicting the CO2 solubility in ILs. In addition, the relationship between features and dissolved CO2 capacity was analyzed through model interpretation to retrieve physical insights for the underlying system. This work provided a new predictive tool for enriching and refining data on CO2 solubility in ILs and for solving phase equilibrium problems.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Due to the equivalent pressure is extremely high (up to several thousand bar), it is hard to determine the compressibility of ILs by using experimental measurements, and thus model prediction can be a viable option. A lot of theoretical models have been developed to predict the properties of ILs, which can be used as theoretical tools to screen ILs, such as Conductor-like Screening Model for Real Solvents (COSMO-RS), COSMO segment activity coefficient model (COSMO-SAC), Soave Redlich Kwong, Universal Quasi-Chemical Model, and so on (Maiti, 2009;Gonzalez-Miquel et al, 2011;Lee and Lin, 2015;Farahipour et al, 2016;Kamgar and Rahimpour, 2016;Theo et al, 2016). However, none of them can be used at high pressures.…”

Section: Introductionmentioning

confidence: 99%

Screening ionic liquids for developing advanced immobilization technology for CO2 separation

et al. 2022

View full text Add to dashboard Cite

Developing immobilized-ionic liquids (ILs) sorbents is important for CO2 separation, and prior theoretically screening ILs is desirable considering the huge number of ILs. In this study, the compressibility of ILs was proposed as a new and additional index for screening ILs, and the developed predictive theoretical model, i.e., electrolyte perturbed-chain statistical associating fluid theory, was used to predict the properties for a wide variety of ILs in a wide temperature and pressure range to provide systematic data. In screening, firstly, the isothermal compressibilities of 272 ILs were predicted at pressures ranging from 1 to 6,000 bar and temperatures ranging from 298.15 to 323.15 K, and then 30 ILs were initially screened. Subsequently, the CO2 absorption capacities in these 30 ILs at temperatures from 298.15 to 323.15 K and pressures up to 50 bar were predicted, and 7 ILs were identified. In addition, the CO2 desorption enthalpies in these 7 ILs were estimated for further consideration. The performance of one of the screened ILs was verified with the data determined experimentally, evidencing that the screen is reasonable, and the consideration of IL-compressibility is essential when screening ILs for the immobilized-IL sorbents.

show abstract

“…In order to understand and predict the phase behavior of CO 2 and ionic liquid mixtures, Perturbed Hard Sphere Chain Equation of State (PHSC) has been selected to simulate the CO 2 absorption in a series of ionic liquids [7]. CO 2 solubility in ionic liquids had been calculated based on two thermodynamic models, namely the UNIQUAC model and the quantum model, based on COSMO-RS theory of interacting molecular surface charge [8]. Venkatramanet et al [9] used molecular descriptors based on quantum chemistry computations to predict the solubility of CO 2 in different ionic liquids.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

2020

View full text Add to dashboard Cite

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

show abstract

Prediction of CO2 solubility in ionic liquids with QM and UNIQUAC models

Cited by 17 publications

References 20 publications

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

Screening ionic liquids for developing advanced immobilization technology for CO2 separation

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Contact Info

Product

Resources

About

Prediction of CO2 solubility in ionic liquids with QM and UNIQUAC models

Cited by 17 publications

References 20 publications

Prediction of CO2 solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

Prediction of CO2 solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

Screening ionic liquids for developing advanced immobilization technology for CO2 separation

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Contact Info

Product

Resources

About

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding

Prediction of CO₂ solubility in ionic liquids via convolutional autoencoder based on molecular structure encoding