Adsorption-Based Separation of Near-Azeotropic MixturesA Challenging Example for High-Throughput Development of Adsorbents

Adsorption
of gas mixtures is central to adsorption-based gas separations,
and the number of adsorbate mixture/adsorbent systems that exist is
staggering. Because examples of machine learning (ML) models predicting
single-component adsorption of arbitrary molecules in large libraries
of crystalline adsorbents have been developed, it is interesting to
determine whether these models can accurately predict mixture adsorption.
Here, we use molecular simulations to generate mixture adsorption
data with a set of 12 near-azeotropic molecules in a diverse set of
MOFs. These data provide a challenging example for any method to rapidly
predict mixture adsorption in MOFs. We combine a previous ML single-component
isotherm model with ideal adsorbed solution theory (IAST) to make
predictions that can be compared directly with molecular simulation
data for these adsorbed mixtures. This combination of ML and IAST
illustrates the scope that is available with these methods, but the
accuracy of the resulting predictions is disappointing. By examining
the same examples with IAST based on minimal molecular simulation
data for single-component isotherms, we show that having an accurate
description of adsorption in the dilute loading limit is critical
to being able to accurately predict mixture adsorption. This observation
points to a useful direction for future work developing robust ML
models of adsorption isotherms for diverse collections of molecules
and adsorbents.