To rank the performance
of materials for a given carbon capture
process, we rely on pure component isotherms from which we predict
the mixture isotherms. For screening a large number of materials,
we also increasingly rely on isotherms predicted from molecular simulations.
In particular, for such screening studies, it is important that the
procedures to generate the data are accurate, reliable, and robust.
In this work, we develop an efficient and automated workflow for a
meticulous sampling of pure component isotherms. The workflow was
tested on a set of metal–organic frameworks (MOFs) and proved
to be reliable given different guest molecules. We show that the coupling
of our workflow with the Clausius–Clapeyron relation saves
CPU time, yet enables us to accurately predict pure component isotherms
at the temperatures of interest, starting from a reference isotherm
at a given temperature. We also show that one can accurately predict
the CO2and N2mixture isotherms using ideal
adsorbed solution theory (IAST). In particular, we show that IAST
is a more reliable numerical tool to predict binary adsorption uptakes
for a range of pressures, temperatures, and compositions, as it does
not rely on the fitting of experimental data, which typically needs
to be done with analytical models such as dual-site Langmuir (DSL).
This makes IAST a more suitable and general technique to bridge the
gap between adsorption (raw) data and process modeling. To demonstrate
this point, we show that the ranking of materials, for a standard
three-step temperature swing adsorption (TSA) process, can be significantly
different depending on the thermodynamic method used to predict binary
adsorption data. We show that, for the design of processes that capture
CO2from low concentration (0.4%) streams, the commonly
used methodology to predict mixture isotherms incorrectly assigns
up to 33% of the materials as top-performing.