(682h) Computational Screening of Zeolites for Gas Separation Applications from Multi-Component Mixtures

Authors: 
Iyer, S. S., Dow Inc.
Demirel, S. E., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
There is abundant literature on high-throughput in-silico screening of micro-porous materials such as zeolites and metal organic frameworks for various gas separation applications.1–5 Recent efforts have been directed towards the combined computational screening of candidate materials and process optimization for various gas separation processes, e.g., CO2 capture6, natural gas purification7, and hydrogen sulfide removal8. To properly evaluate the applicability of a process, it is important to not just perform process level optimization, but also simultaneously determine candidate materials that are most suitable for different process configurations. Though simultaneous materials and process optimization is useful for optimizing well-established processes, it may not be feasible for quick evaluation of novel and emerging technologies such as integrated carbon capture and conversion9, new sorption-enhanced reaction processes10, and combined separation and storage11. To develop novel and intensified processes to improve productivity and efficiency, it is necessary to leverage on properties (i.e. storage, selectivity) of different gases on different materials. For separation of the desired gas from a multi-component gas mixture, the working capacity of the material depends on the relative selectivity parameters, nature of isotherms and the concentrations of the gases.

To this end, we have performed Grand-Canonical Monte Carlo simulations for gases such as CO2, CH4, CO, H2, H2S, H2O and N2 to obtain gas (adsorbate) loading on existing pure-silica zeolites (adsorbents) from the IZA-SC database for different temperatures (298, 323 and 373 K) and pressures (1.3, 5.3, 10.7, 21.3, 42.7, 85.3, 101.3, 266.6 15, 580, 6500 kPa). These compute-intensive simulations are performed on a high performance computing cluster named Ada. Based on the data obtained from these molecular simulations, selectivity parameters are then obtained for different combination of gases (CO2/N2, CH4/N2, CO2/CH4, H2/CO, H2S/CH4, H2/CH4, CO2/N2/H2O, CO2/H2S/CH4, etc.) and their concentrations in feed mixtures. The data from molecular simulations is fitted to single, dual, multi-site Langmuir, extended Langmuir, Freundlich and Toth adsorption isotherm models to determine the best fitting model for each gas-zeolite data set. The isotherm parameters are obtained by solving the minimization problem of the least-square error between the data and the model prediction to global optimality. The adsorption isotherm model is then incorporated into the process model.

The top zeolites for each application of gas separation from a multi-component mixture of different concentrations are then rank ordered based on different materials-centric metrics (adsorption selectivity, saturation loadings, working capacity, heat of adsorption, Henry’s constant, etc.). In addition, zeolites which would not be suitable for a particular application are also determined. The information gathered will enable the design of novel process configurations for different gas separation applications.

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