Gas Solubility in Ionic Liquids: UNIFAC-IL Model Extension

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Size Matters: A Computational Study of Hydrogen Absorption in Ionic Liquids

We combined both density functional theory and classical molecular dynamics simulations to investigate the molecular mechanisms governing hydrogen solvation in a total of 12 ionic liquids. Overall, the analysis of the structural properties under high temperature and pressure conditions revealed weak interactions between hydrogen and the ionic liquids, with a slight preference of this gas to be placed at the apolar domains. Interestingly, those ionic liquids comprising nitrate anions allow the accommodation of hydrogen molecules also in the polar areas. The study of the hydrogen velocity autocorrelation functions supports this observation. In addition, the structure of all of the tested ionic liquids was almost insensitive to the addition of hydrogen, so the available free volume and cavity formation are presumably the most important factors affecting solubility.

Condensable Gases Capture with Ionic Liquids

Condensable gases are the sum of condensable and volatile steam or organic compounds, including water vapor, which are discharged into the atmosphere in gaseous form at atmospheric pressure and room temperature. Condensable toxic and harmful gases emitted from petrochemical, chemical, packaging and printing, industrial coatings, and mineral mining activities seriously pollute the atmospheric environment and endanger human health. Meanwhile, these gases are necessary chemical raw materials; therefore, developing green and efficient capture technology is significant for efficiently utilizing condensed gas resources. To overcome the problems of pollution and corrosion existing in traditional organic solvent and alkali absorption methods, ionic liquids (ILs), known as "liquid molecular sieves", have received unprecedented attention thanks to their excellent separation and regeneration performance and have gradually become green solvents used by scholars to replace traditional absorbents. This work reviews the research progress of ILs in separating condensate gas. As the basis of chemical engineering, this review first provides a detailed discussion of the origin of predictive molecular thermodynamics and its broad application in theory and industry. Afterward, this review focuses on the latest research results of ILs in the capture of several important typical condensable gases, including water vapor, aromatic VOCs (i.e., BTEX), chlorinated VOC, fluorinated refrigerant gas, low-carbon alcohols, ketones, ethers, ester vapors, etc. Using pure IL, mixed ILs, and IL + organic solvent mixtures as absorbents also briefly expanded the related reports of porous materials loaded with an IL as adsorbents. Finally, future development and research directions in this exciting field are remarked.

Intelligent modeling of hydrogen sulfide solubility in various types of single and multicomponent solvents

This study aims to study the solubility of acid gas, i.e., hydrogen sulfide (H₂S) in different solvents. Three intelligent approaches, including Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Radial Basis Function (RBF) were used to construct reliable models based on an extensive databank comprising 5148 measured samples from 54 published sources. The analyzed data cover 95 single and multicomponent solvents such as amines, ionic liquids, electrolytes, organics, etc., in broad pressure and temperature ranges. The proposed models require just three simple input variables, i.e., pressure, temperature and the equivalent molecular weight of solvent to determine the solubility. A competitive examination of the novel models implied that the GPR-based one gives the most appropriate estimations with excellent AARE, R² and RRMSE values of 4.73%, 99.75% and 4.83%, respectively for the tested data. The mentioned intelligent model also performed well in describing the physical behaviors of H₂S solubility at various operating conditions. Furthermore, analyzing the William's plot for the GPR-based model affirmed the high reliability of the analyzed databank, as the outlying data points comprise just 2.04% of entire data. In contrast to the literature models, the newly presented approaches proved to be applicable for different types of single and multicomponent H₂S absorbers with AAREs less than 7%. Eventually, a sensitivity analysis based on the GPR model reflected the fact that the solvent equivalent molecular weight is the most influential factor in controlling H₂S solubility.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Absorption has always been an attractive process for removing hydrogen sulfide (H₂S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H₂S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H₂S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H₂S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H₂S solubility in ILs and possesses R², RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H₂S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM⁺ and Tf₂N^-, i.e., [OMIM][Tf₂N] ionic liquid, is the best choice for H₂S capture among the investigated absorbents. The H₂S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Comparison of CP-PC-SAFT and SAFT-VR-Mie in Predicting Phase Equilibria of Binary Systems Comprising Gases and 1-Alkyl-3-methylimidazolium Ionic Liquids

This study compares performances of the Critical Point-based revision of Perturbed-Chain SAFT (CP-PC-SAFT) and the SAFT of Variable Range and Mie Potential (SAFT-VR-Mie) in predicting the available data on VLE, LLVE, critical loci and saturated phase densities of systems comprising CO, O2, CH4, H2S, SO2, propane, the refrigerants R22, R23, R114, R124, R125, R125, R134a, and R1234ze(E) and ionic liquids (ILs) with 1-alkyl-3-methylimidazolium ([Cnmim]+) cations and bis(trifluoromethanesulfonyl)imide ([NTf2]-), tetrafluoroborate ([BF4]-) and hexafluorophosphate ([PF6]-) anions. Both models were implemented in the entirely predictive manner with k12 = 0. The fundamental Global Phase Diagram considerations of the IL systems are discussed. It is demonstrated that despite a number of quantitative inaccuracies, both models are capable of reproducing the regularities characteristic for the considered systems, which makes them suitable for preliminary estimation of selectivity of the ILs in separating various gases.