2019
DOI: 10.1021/acs.iecr.9b05363
|View full text |Cite
|
Sign up to set email alerts
|

How Well Do Approximate Models of Adsorption-Based CO2 Capture Processes Predict Results of Detailed Process Models?

Abstract: Appropriate selection of adsorbent materials is essential in developing adsorption-based processes such as CO 2 capture. Approximate methods to evaluate material candidates exist using adsorbent evaluation metrics or simplified process models. These approximate methods do not, of course, completely describe the performance of adsorbents in real separation processes. Here, we assess the correlations between approximate predictions and detailed process models of pressure swing adsorption (PSA) at subambient temp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
53
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
5
2
1
1

Relationship

2
7

Authors

Journals

citations
Cited by 58 publications
(53 citation statements)
references
References 90 publications
(230 reference statements)
0
53
0
Order By: Relevance
“…where: α ab is the ideal selectivity of component a over b, n x is the amount adsorbed of component x [mol kg −1 ], and y x is the mole fraction of component x in the feed. It has since been demonstrated that simple metrics do not adequately assess an adsorbent's performance (Maring and Webley, 2013;Rajagopalan et al, 2016;Rajagopalan and Rajendran, 2018;Park et al, 2019;Burns et al, 2020;Danaci et al, 2020;Yancy-Caballero et al, 2020). Significant improvement over these metrics can be achieved by employing an equilibrium (or screening) process model (Maring and Webley, 2013;Joss et al, 2015;Subramanian Balashankar et al, 2019).…”
Section: Milligrams To Kilotonnesmentioning
confidence: 99%
“…where: α ab is the ideal selectivity of component a over b, n x is the amount adsorbed of component x [mol kg −1 ], and y x is the mole fraction of component x in the feed. It has since been demonstrated that simple metrics do not adequately assess an adsorbent's performance (Maring and Webley, 2013;Rajagopalan et al, 2016;Rajagopalan and Rajendran, 2018;Park et al, 2019;Burns et al, 2020;Danaci et al, 2020;Yancy-Caballero et al, 2020). Significant improvement over these metrics can be achieved by employing an equilibrium (or screening) process model (Maring and Webley, 2013;Joss et al, 2015;Subramanian Balashankar et al, 2019).…”
Section: Milligrams To Kilotonnesmentioning
confidence: 99%
“…It has been shown earlier that material separation performances and process level objectives do not necessarily correlate well with each other. 90,91 Therefore, as a further evaluation, practical metrics such as purity, recovery, and capture cost should be considered to holistically understand the potential benefits of the materials discussed herein. Recently, Danaci et al 92 revealed the correlations between various structural features, adsorption amounts of weakly interacting gas (i.e., N 2 ) and purity levels of strongly interacting gas (i.e., CO 2 ) in their work bridging molecular modeling to process modeling.…”
Section: Resultsmentioning
confidence: 99%
“…Adsorption equilibrium information is a fundamental resource to evaluate the performance of sorbent candidates for application at process scale. 36 We estimated CO 2 and N 2 single-component adsorption isotherms by Grand Canonical Monte Carlo (GCMC) simulations 37,38 in UiO-66 and MIL-101(Cr). This technique has been extensively used to estimate the adsorption properties of CO 2 and similar species in a range of porous sorbent materials.…”
Section: Sorbent Performance Estimationmentioning
confidence: 99%
“…UiO-66 and MIL-101(Cr) are two MOFs that show some promise for sub-ambient CO 2 capture. 36,41 Both materials are stable in liquid water, which is a prerequisite for their spinning into heat managed fiber sorbent structures. In addition, both materials have shown the ability to be scaled up to the 10-1000 g batch scale.…”
Section: Sub-ambient Fiber Sorbent Psamentioning
confidence: 99%