(682h) Computational Screening of Zeolites for Gas Separation Applications from Multi-Component Mixtures
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, Henryâs 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.
(1) Martin, R. L.; Willems, T. F.; Lin, L.-C.; Kim, J.; Swisher, J. A.; Smit, B.; Haranczyk, M. Similarity-Driven Discovery of Zeolite Materials for Adsorption-Based Separations. Chemphyschem A Eur. J. Chem. Phys. Phys. Chem. 2012, 13 (16), 3595â3597.
(2) Colon, Y. J.; Snurr, R. Q. High-Throughput Computational Screening of Metal-Organic Frameworks. Chem. Soc. Rev. 2014, 43 (16), 5735â5749.
(3) Lin, L.-C.; Berger, A. H.; Martin, R. L.; Kim, J.; Swisher, J. A.; Jariwala, K.; Rycroft, C. H.; Bhown, A. S.; Deem, M. W.; Haranczyk, M.; et al. In silico Screening of Carbon-Capture Materials. Nat Mater 2012, 11 (7), 633â641.
(4) Krishna, R.; van Baten, J. M. In silico Screening of Metal-Organic Frameworks in Separation Applications. Phys. Chem. Chem. Phys. 2011, 13 (22), 10593â10616.
(5) Wu, D.; Wang, C.; Liu, B.; Liu, D.; Yang, Q.; Zhong, C. Large-Scale Computational Screening of Metal-Organic Frameworks for CH4/H2 Separation. AIChE J. 2012, 58 (7), 2078â2084.
(6) Hasan, M. M. F.; First, E. L.; Floudas, C. A. Cost-Effective CO2 Capture Based on in silico Screening of Zeolites and Process Optimization. Phys. Chem. Chem. Phys. 2013, 15 (40), 17601â17618.
(7) First, E. L.; Hasan, M. M. F.; Floudas, C. A. Discovery of Novel Zeolites for Natural Gas Purification through Combined Material Screening and Process Optimization. AIChE J. 2014, 60 (5), 1767â1785.
(8) Liu, T.; First, E. L.; Hasan, M. M. F.; Floudas, C. A. A Multi-Scale Approach for the Discovery of Zeolites for Hydrogen Sulfide Removal. Comput. Chem. Eng. 2016, 91, 206â218.
(9) Iyer, S. S.; Bajaj, I.; Balasubramanian, P.; Hasan, M. M. F. Modular Process Intensification of Carbon Capture and Conversion to Syngas. Submitted.
(10) Carvill, B. T.; Hufton, J. R.; Anand, M.; Sircar, S. Sorption-Enhanced Reaction Process. AIChE J. 1996, 42 (10), 2765â2772.
(11) Iyer, S. S.; Hasan, M. M. F. A Novel Plug-and-Store Technology for Natural Gas Purification and Strage. In CAMX 2015 - Composites and Advanced Materials Expo; 2015.