(754e) Property Modeling of Ionic Liquids for Gas Separation Processes

With increasing calls for “green” and sustainable technologies, ionic liquids (ILs) have been receiving much attention as a potential solvent for gas separation tasks due to their unique properties. Therefore, for a sustainable design point of view, IL-gas absorption has been identified as a replacement for the energy intensive distillation based gas separation.

One of the promising advantages of ILs is their negligible vapor pressure, which eliminates the risk of contamination of the gas stream and the potential loss of ILs. Many researchers have evaluated the possibility of ILs as a gas absorption solvent by measuring the solubility of various gases in different ILs. The most common studies concentrated on the CO₂ capture capacities of different ILs, which points to the potential of an IL-based gas decarbonization technology (Liu et al, 2016¹). Encouraged by good absorption of CO₂ with IL, new studies have focused on the solubility of other gases in ILs. As the potential number of ILs that can be generated by combining cations and anions is enormous, experimental studies of gas solubility in ILs only provide a fraction of the data-knowledge needed to search over a wide spectrum of ILs to find the most appropriate for gas separation tasks. Not only the solubility of the selected gas in the solvent (IL or other chemicals) is important but for a gas separation task, the selectivity of the IL solubility for the gases to be separated is also very important, together with other pure component properties of the IL, such as viscosity, stability, density, etc. Therefore, before a selection-screening method for ILs for use in absorption based gas separation tasks can be developed, the used properties must be available. The objective is to identify those ILs that would match, a priori, the desired properties of the gas absorption solvent and thereby lead to an efficient gas separation process.

In this work, a property modeling tool has been developed for use in screening method for selection of the most appropriate IL for specific absorption based gas separation tasks. The property models are available in two scales. At the lower scale, the solubility data is generated in the form of Henry’s constants of gases in various ILs through the COSMO-RS model (Klamt et al. 2000²) and/or molecular simulations (Huang et al.2013³). After evaluating their accuracy, they are included in a database of solubility of gases in ILs together with IL properties. These data are then used in a mezo-scale predictive engineering models (like the group contribution methods) for the same properties. Note that the role of the smaller-scale property modelling is to enlarge the potential search space for the ILs. Solubility, Henry’s constant, selectivity, viscosity, density, surface tension, IL-stability, vapor pressure, for a given set of desired (target) properties, the screening method is then able to find the ILs that match the target properties using a search algorithm similar to that of Harper et al. 2000⁴.

The presentation will highlight the contents of the database, the validation of the generated data and the developed property models together with applications of the screening-selection method for IL as solvents for various absorption based gas separation problems. More specifically, separations of gases like H₂, CO₂, CH₄, C₂H₄, C₂H₆, with IL based absorption will be illustrated.

References

1. Liu, X., Huang, Y., Zhao, Y., Gani, R., Zhang, X., & Zhang, S. (2016). Ionic Liquid Design and Process Simulation for Decarbonization of Shale Gas. Industrial & Engineering Chemistry Research, 55(20), 5931-5944.
2. Klamt, A., & Eckert, F. (2000). COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria, 172(1), 43-72.
3.  Huang, Y., Dong, H., Zhang, X., Li, C., & Zhang, S. (2013). A new fragment contribution‐corresponding states method for physicochemical properties prediction of ionic liquids. AIChE Journal, 59(4), 1348-1359.
4.  Harper, P. M., & Gani, R. (2000). A multi-step and multi-level approach for computer aided molecular design. Computers & Chemical Engineering, 24(2), 677-683.