Integrating Macrostate Probability Distributions with Swing Adsorption Modeling for Binary/Ternary Gas Separation
Journal
Industrial and Engineering Chemistry Research
Journal Volume
64
Journal Issue
52
Start Page
25017
End Page
25033
ISSN
08885885
Date Issued
2025-12-31
Author(s)
Abstract
Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remains a central challenge for designing energy-efficient, adsorption-based separation processes. Traditional approaches, including model fitting and ideal adsorbed solution theory (IAST), often fail to balance accuracy, computational efficiency, and transferability under process-relevant conditions. Here, we introduce a material-to-process modeling framework that integrates macrostate probability distributions (MPDs) from flat-histogram Monte Carlo simulations with rigorous cyclic process optimization. MPDs directly capture the joint occupancy distributions of adsorbates, producing a reweightable landscape that enables high-fidelity mixture adsorption equilibria without repeated simulations or model assumptions. We show that coupling this statistical mechanical foundation with process modeling delivers accurate and computationally efficient evaluations for binary and ternary gas mixture separations. This integration establishes MPD-based modeling as a generalized method for predictive multicomponent adsorption equilibria, accelerating the discovery and design of adsorbent materials for carbon capture and other separation challenges.
Publisher
American Chemical Society
Type
journal article