Numerical study of the dissolution of carbon dioxide in an ionic liquid

Sekerci-Cetin, Melek; Emek, Omer Baris; Yildiz, Emine Elif; Unlusu, Betul

doi:10.1016/j.ces.2016.03.024

Cited by 7 publications

(13 citation statements)

References 30 publications

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“…3,10,13,14,16−20,22,23,53,56−62 However, this simplification is often not supported by the real measurements of absorption of different gases into liquids. 23,25,28,63,64 To solve the problem with gas swelling into liquid with an unknown gas−liquid interface, it is necessary to specify an additional condition for the diffusion equation. Such a condition was formulated based on the mass conservation of the dissolved gas and represented in the form of the Stefan's type boundary value condition at the moving interface.…”

Section: Discussionmentioning

confidence: 99%

“…Such multiplex character of the solvent gas–liquid systems is described using a free boundary problem for the diffusion equation with unknown diffusion coefficient. In practical applications, the problem of solvent gas–liquid interaction is often solved using simplified approach that neglects the gas swelling into the liquid phase and corresponding gas–liquid interface movement. ,,,,− ,,,,− However, this simplification is often not supported by the real measurements of absorption of different gases into liquids. ,,,, To solve the problem with gas swelling into liquid with an unknown gas–liquid interface, it is necessary to specify an additional condition for the diffusion equation. Such a condition was formulated based on the mass conservation of the dissolved gas and represented in the form of the Stefan’s type boundary value condition at the moving interface. ,− , An alternative approach for identification of the unknown gas–liquid interface was applied based on the moving grid size refinement .…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Babak

Kantzas

2018

Ind. Eng. Chem. Res.

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The multifaceted process of gas absorption and diffusion of dissolved gases has several useful applications in engineering, medicine, pharmaceutics, and science. A new systematic study for modeling of this process using an inverse problem for concentration dependent diffusion with free boundary at the gas−liquid interface is presented in this work. The study includes the model derivation based on the Fick's second law of diffusion, the Henry's absorption law, the Stefan's principles of free boundary problems, and the development of new techniques for estimation of concentration dependent diffusion coefficient and the gas absorption coefficient governing propagation of gas−liquid interface due to absorption. The developed estimation technique utilized the dissolved gas concentration profiles measured and different time moments and is applied to real X-ray CT measurement data for a binary gas liquid mixture containing dimethyl ether and bitumen.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Babak

Kantzas

2018

Ind. Eng. Chem. Res.

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“…ANSYS FLUENT is used to develop the transport model of the system where two phases are separated by a moving boundary. [31] The continuity equation solved in the entire flow domain is as follows:…”

Section: Computation Modelmentioning

confidence: 99%

Diffusion coefficients and phase equilibria of carbon dioxide–ionic liquid, 1‐butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][PF₆]) system

et al. 2023

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This article presents the mutual diffusion coefficients of a carbon dioxide–ionic liquid, [bmim][PF6], system at temperatures of 313.15 and 323.15 K and pressures of 5 and 8 MPa. In order to estimate the diffusion coefficients, we have carried out experiments to find time‐dependent carbon dioxide solubilities in the ionic liquid and then fit a transport model to the data. In a system containing high pressure carbon dioxide and ionic liquid, carbon dioxide dissolves in the liquid until its equilibrium mole fraction is reached. During this process, the position of the liquid–vapour interface and the density of the liquid phase change. To account for the variation in liquid density, an equation fit to the experimental density data is included in the transport model. To track the moving interface, the volume‐of‐fluid method is used. The diffusivities at dilute concentration and at thermodynamic phase equilibrium are determined and compared with the literature values and those obtained from correlations.

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“…ILs are one of the most prominent eco-friendly solvents. ILs might be regarded as attractive compounds with various potential industrial uses because of these exceptional properties 5 – 7 . Recent works on ILs have concentrated on structure–property correlations to intelligently develop ILs for special purposes.…”

Section: Introductionmentioning

confidence: 99%

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Nakhaei-Kohani

Atashrouz

Hadavimoghaddam

et al. 2022

Sci Rep

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Ionic liquids (ILs) have emerged as suitable options for gas storage applications over the past decade. Consequently, accurate prediction of gas solubility in ILs is crucial for their application in the industry. In this study, four intelligent techniques including Extreme Learning Machine (ELM), Deep Belief Network (DBN), Multivariate Adaptive Regression Splines (MARS), and Boosting-Support Vector Regression (Boost-SVR) have been proposed to estimate the solubility of some gaseous hydrocarbons in ILs based on two distinct methods. In the first method, the thermodynamic properties of hydrocarbons and ILs were used as input parameters, while in the second method, the chemical structure of ILs and hydrocarbons along with temperature and pressure were used. The results show that in the first method, the DBN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.0054 and 0.9961, respectively, and in the second method, the DBN model with RMSE and R2 values of 0.0065 and 0.9943, respectively, have the most accurate predictions. To evaluate the performance of intelligent models, the obtained results were compared with previous studies and equations of the state including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Redlich–Kwong (RK), and Zudkevitch–Joffe (ZJ). Findings show that intelligent models have high accuracy compared to equations of state. Finally, the investigation of the effect of different factors such as alkyl chain length, type of anion and cation, pressure, temperature, and type of hydrocarbon on the solubility of gaseous hydrocarbons in ILs shows that pressure and temperature have a direct and inverse effect on increasing the solubility of gaseous hydrocarbons in ILs, respectively. Also, the evaluation of the effect of hydrocarbon type shows that increasing the molecular weight of hydrocarbons increases the solubility of gaseous hydrocarbons in ILs.

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Numerical study of the dissolution of carbon dioxide in an ionic liquid

Cited by 7 publications

References 30 publications

Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Diffusion coefficients and phase equilibria of carbon dioxide–ionic liquid, 1‐butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][PF₆]) system

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

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Numerical study of the dissolution of carbon dioxide in an ionic liquid

Cited by 7 publications

References 30 publications

Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Estimation of Gas Absorption and Diffusion Coefficients for Dissolved Gases in Liquids

Diffusion coefficients and phase equilibria of carbon dioxide–ionic liquid, 1‐butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][PF6]) system

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Contact Info

Product

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Diffusion coefficients and phase equilibria of carbon dioxide–ionic liquid, 1‐butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][PF₆]) system