(747g) Process Optimization-Centric Design and Screening of Nanoporous Adsorbents for Gas Separations

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
There are several works on computational screening of nano-porous materials such as zeolites and metal-organic frameworks (MOFs) for gas separation applications including CO2 capture and natural gas purification1,2. Other studies involve developing novel adsorbent screening metrics to compare adsorbents without performing process simulations3-5. While these are computationally inexpensive, they may not be enough to check the feasibility with respect to specified process constraints. Recent efforts have focused on obtaining optimal materials by performing material screening along with process optimization6,7.

In this work, we determine the optimal material properties for a given process operation (e.g. pressure swing adsorption) through optimization of a completely discretized algebraic model of the process. This involves optimizing a non-linear programming problem with material properties as decision variables. The adsorbent properties such as isotherms, heat of adsorption and selectivity of materials (e.g. zeolites) in existing databases are calculated using molecular simulations and their range is provided as bounds for the optimization. The materials from the database with properties closest to the obtained optimal material properties can then be rigorously explored. This approach eliminates the need for including individual material alternatives in the optimization formulation. Revised optimal properties for any changes in process and feed conditions can also be determined easily. For example, in the case of an optimal zeolite (SBN) obtained through material screening for a simultaneous methane storage and separation application8, a 1% change in isotherm parameters results in a 3.27% increase in optimal methane storage from 186.13 v(STP)/v to 192.23 v(STP)/v. The structural similarity9 between materials possessing similar adsorption properties is used to then predict the geometric properties including pore size, channel diameter, void fraction and surface area corresponding to the optimal adsorbent properties by using a data-driven surrogate-model based approach10. Such a process optimization-centric methodology can help in the design of adsorbents tailored to the specific requirements of gas separation process applications.


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(2) Krishna, R.; van Baten, J. M. In silico Screening of Metal-Organic Frameworks in Separation Applications. Phys. Chem. Chem. Phys. 2011, 13 (22), 10593–10616.

(3) Wiersum, A. D., Chang, J. S., Serre, C. and Llewellyn, P. L. An Adsorbent Performance Indicator as a First Step Evaluation of Novel Sorbents for Gas Separations: Application to Metal-organic Frameworks. Langmuir, 2013, 29(10), 3301-3309.

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(5) Ga, S.; Jang, H. and Lee, J. H. New Performance Indicators for Adsorbent Evaluation Derived from a Reduced Order Model of an Idealized PSA Process for CO2 capture. Comput. Chem. Eng. 2017, 102, 188-212.

(6) Hasan, M. M. F.; First, E. L. and Floudas, C. A. Cost-effective CO2 Capture based on In silico Screening of Zeolites and Process Optimization. Phys. Chem. Chem. Phys., 2013, 15 (40), 17601-17618.

(7) Hasan, M. M. F.; First, E. L. and Floudas, C. A. Discovery of Novel Zeolites and Multi-zeolite Processes for p-xylene Separation using Simulated Moving Bed (SMB) chromatography. Chem. Eng. Sci., 2017, 159, 3-17.

(8) 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.

(9) 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. Chem. Phys. Chem A Eur. J. Chem. Phys. Phys. Chem., 2012, 13 (16), 3595–3597.

(10) 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.