Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 61, Issue 1, Pages 727-739Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.1c03876
Keywords
-
Categories
Funding
- Department of Energy's Office of Energy Efficient and Renewable Energy's Advanced Manufacturing Office [DE-EE0007888]
Ask authors/readers for more resources
This paper systematically evaluates the accuracy of the ideal adsorbed solution theory (IAST) for gas adsorption using a large collection of binary experimental data, which will be valuable for future efforts to test or develop mixing theories that improve upon FAST. This analysis includes data from 63 gas mixtures of 37 different molecular species and 174 different adsorbents, making it the most systematic evaluation to date of the accuracy of IAST for gas adsorption.
Quantifying the adsorption of chemical mixtures in porous adsorbents is critical to developing these materials for useful separation applications. The ideal adsorbed solution theory (IAST) is the most widely applied mixing theory for predicting mixture adsorption using singlecomponent adsorption data, but a perceived lack of experimental data has limited previous efforts to explore the accuracy of FAST in a systematic way. In this paper, we take advantage of a large collection of binary experimental data for gas adsorption that became available recently (Cai. X et al., Ind. Eng. Chem. Res. 2021, 60; 639) to tackle this issue. We identify more than 400 examples in which binary adsorption data and single-component data are available in the same publication and apply LAST to all these examples. This analysis includes experimental data from 63 gas mixtures of 37 different molecular species and 174 different adsorbents. In addition to being the most systematic evaluation to date of the accuracy of LAST for gas adsorption, these data will be valuable for future efforts to test or develop mixing theories that improve upon FAST.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available