Prediction of Multicomponent Gas Adsorption Equilibrium Using Ideal Adsorbed Solution Theory for Direct Air Capture

Adsorption equilibrium is an important metric used to assess adsorbent performance for gas mixture separation processes. Gas adsorption processes such as carbon capture are becoming more urgent as climate change and global warming accelerate. To speed up and reduce the cost of research on adsorbent materials and adsorption processes, I developed an open-source Python code that generates mixed gas adsorption equilibrium data using pure gas adsorption isotherms based on the ideal adsorbed solution theory (IAST). The major efforts of this M.S. research were placed on adding additional components to the mixture models since most other publications focused on binary gas mixtures. Generated mixed-gas equilibrium data were compared to experimentally collected data in order to validate the multicomponent IAST model and to determine the accuracy of the computer codes developed in this work. Additional mixed-gas equilibrium data were then generated and analyzed for trends in the data for humid flue gas conditions, natural gas processing conditions, and hydrogen gas purification conditions. For humid flue gas conditions, neither the analyzed Mg-MOF-74 nor the Zeolite 13X were shown to be suitable for use. For natural gas processing conditions, the Zeolite 13X was determined to be a much better candidate for use than the MIL-101. For hydrogen gas purification conditions, the Zeolite 5A was determined to be a better adsorbent for use than CD-AC due to the Zeolite 5A’s much lower adsorption of H2.