(449d) Inverse Design of Nanoporous Adsorbents for Gas Separation Applications

Authors: 
Iyer, S. S., Dow Inc.
Bajaj, I., Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Significant research efforts have been undertaken for in silico screening of nano-porous materials in applications such as methane storage, carbon capture1-2. Both theoretical insights and data-driven approaches have been used to develop structure property relationships for properties such as gas storage, selectivity, heat of adsorption3-6. Balancing multiple material property tradeoffs is important for both commercial and novel applications. Optimizing a completely algebraic formulation of a process can provide optimal material property targets. While these may not correspond directly to an existing material, possible structures can be predicted inversely by exploiting similarity7 between structures and properties of adsorbent materials.

In this work, the optimal adsorbent properties (e.g. adsorption isotherms, heat of adsorption etc.) are determined by optimizing a completely discretized non-linear programming modelof a pressure swing adsorption process for a desired objective such as cost or productivity subject to process constraints such as purity and recovery. The bounds on the material properties are set based on the isotherm data obtained from Grand Canonical Monte Carlo (GCMC) simulations of structures in the IZA-SC database. This approach is advantageous as all the material alternatives do not have to be individually considered. Moreover, the optimal material properties can be recalculated for changes in process and feed conditions with relative ease. For the reverse estimation of structural properties, a surrogate model based approach8 is employed. It relates the differences in structural properties such as channel diameter, pore size, accessible surface area and volume between pairs of pure silica zeolites to the difference in their adsorption properties. Based on this, the appropriate structural features corresponding to the target material properties are then obtained. The salient feature of this approach is to design materials for optimal operation of processes such as CO2 capture, methane storage.

References:

(1) Colon, Y. J.; Snurr, R. Q. High-Throughput Computational Screening of Metal-Organic Frameworks. Chem. Soc. Rev., 2014, 43 (16), 5735–5749.

(2) 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. and Smit, B. In silico Screening of Carbon-Capture Materials. Nat. Mater., 2012, 11 (7), 633–641.

(3) Wilmer, C. E.; Farha, O. K.; Bae, Y-S.; Hupp, J. T. and Snurr, R. Q.; Structure–property Relationships of Porous Materials for Carbon Dioxide Separation and Capture, Energy Environ. Sci., 2012, 5, 9849-9856.

(4) Garcia, E. J.; Perez-Pellitero, J.; Jallut, C. and Pirngruber, G. D. How to Optimize the Electrostatic interaction between a Solid Adsorbent and CO2. J. Phys. Chem. C, 2014, 118, 9458-9467.

(5) Bai, P.; Jeon, M. Y.; Ren, L.; Knight, C.; Deem, M. W.; Tsapatsis, M. and Siepmann, J. I. Discovery of Optimal Zeolites for Challenging Separations and Chemical Transformations using Predictive Materials Modeling. Nat. Commun., 2015, 6, 5912.

(6) Fernandez, M.; Boyd, P.G.; Daff, T. D.; Aghaji, M. Z. and Woo, T. K. Rapid and Accurate Machine Learning Recognition of High Performing Metal-Organic Frameworks for CO2 Capture. J. Phys. Chem. Lett. 2014, 5, 3056-3060.

(7) 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, 2012, 13, 3595–3597.

(8) Bajaj, I.; Iyer, S. S. and Hasan, M. M. F. A Trust Region-based Two Phase Algorithm for Constrained Black-box and Grey-box Optimization with Infeasible Initial Point. Comput. Chem. Eng., 2017, doi: 10.1016/j.compchemeng.2017.12.011.