Pressure swing adsorption (PSA) is an advanced technology widely used for gas separation and storage applications. Several types of adsorbents including zeolites, metal organic frameworks (MOFs) and activated carbon have been characterized and synthesized with a wide array of applications in PSA operations [1–4]. Numerous hypothetical zeolites and MOFs can be also conceptualized [5,6]. The available feedstock specifications and the desired product specifications determine the choice of adsorbent and process design. A majority of the previous material-screening studies either use computationally expensive methods, and are able to screen only a few prospective adsorbents, or use significant modeling assumptions thereby leading to uncertainties in the reported results. This incentivizes the search of more predictive methods and metrics that can correctly estimate material performance in a real processing operation. We have developed a high-throughput material screening framework that is capable of quickly screening millions of potential adsorbent materials. The framework integrates a high-fidelity adsorption process model with an artificial neural network (ANN) model to describe breakthrough dynamics in adsorber columns. Detailed Grand Canonical Monte Carlo (GCMC) simulations are performed for obtaining equilibrium adsorption capacity data for different adsorbent-adsorbent pairs. We introduce ANN-based surrogate models for predicting breakthrough dynamics in an adsorber column. The resulting models have much less complexity compared to the detailed first-principles model, and can be leveraged for rapid screening of a large adsorbent database. The developed framework can be used to evaluate material separation performance for different process designs, operating conditions and adsorbent materials. For example, it can predict the so-called “breakthrough time†that can be used as a single metric to capture the effects of both adsorption capacity and selectivity [7]. Breakthrough time also influences the column size and regeneration frequency. This metric is shown to facilitate a more efficient and realistic screening of adsorbent materials as it combines the effects of adsorption capacity and selectivity to indicate PSA performance. To demonstrate the utility of the framework, we screen 196 pure-silica zeolites in the Structure Commission of the International Zeolite Association (IZA-SC) database for post-combustion carbon capture and natural gas purification. Using the framework, we have identified zeolites WEI and JBW as the top two candidate adsorbents, as they are consistently ranked among the top zeolites for both applications.
References
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[4] J.-R. Li, R.J. Kuppler, H.-C. Zhou, Selective gas adsorption and separation in metal--organic frameworks, Chem. Soc. Rev. 38 (2009) 1477–1504.
[5] A.N. Dickey, A.Ö. Yazayd\in, R.R. Willis, R.Q. Snurr, Screening \ce{CO}2/\ce{N2} selectivity in metal-organic frameworks using Monte Carlo simulations and ideal adsorbed solution theory, Can. J. Chem. Eng. 90 (2012) 825–832.
[6] K. Sumida, D.L. Rogow, J.A. Mason, T.M. McDonald, E.D. Bloch, Z.R. Herm, T.-H. Bae, J.R. Long, Carbon dioxide capture in metal--organic frameworks, Chem. Rev. 112 (2012) 724–781.
[7] R. Krishna, J.R. Long, Screening metal-organic frameworks by analysis of transient breakthrough of gas mixtures in a fixed bed adsorber, J. Phys. Chem. C. 115 (2011) 12941–12950.
