Research Article

Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks

Volume: 12 Number: 3 December 31, 2019
TR EN

Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks

Abstract

Xe has been shown to be a promising candidate for anesthetic applications. However,  its high price prevents its usage in clinical industry. An alternative approach is to recover Xe from anesthetic exhale gas mixture and recycle it to the inhale gas stream. Although, many membranes and/or adsorbents have been proposed for recovering anesthetic Xe, using metal organic frameworks (MOFs) for adsorption based separation of  anesthetic Xe exhale gas mixtures has been newly studied. MOFs have  tunable pore sizes, large surface areas, and high porosities which make them potential candidates for gas separation applications. Currently, very little is known about anesthetic Xe recovery  performances of MOFs. We theoretically investigate adsorption based separation of single component and binary mixtures of CO2, Xe, and N2 in three MOFs, namely  CECYOY, SUDBOI, and ZUQPOQ. Single component and binary adsorption isotherms and adsorption selectivities are calculated using Grand Canonical Monte Carlo simulations for each MOF in order to characterize their performances as adsorbents. Results suggest that while MOFs prefer adsorption of CO2 for  CO2/Xe mixture,  Xe adsorption is favorable in the case of Xe/N2 mixture. While SUDBOI shows significantly large CO2 adsorption selectivity for CO2/Xe mixture,  ZUQPOQ has the largest adsorption selectivity for Xe/N2 mixture.

 

Keywords

Thanks

The numerical calculations reported in this paper were fully performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources), located in Turkey.

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Publication Date

December 31, 2019

Submission Date

November 30, 2019

Acceptance Date

December 24, 2019

Published in Issue

Year 2019 Volume: 12 Number: 3

APA
Gurdal, Y. (2019). Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks. Erzincan University Journal of Science and Technology, 12(3), 1705-1714. https://doi.org/10.18185/erzifbed.653429
AMA
1.Gurdal Y. Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks. Erzincan University Journal of Science and Technology. 2019;12(3):1705-1714. doi:10.18185/erzifbed.653429
Chicago
Gurdal, Yeliz. 2019. “Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks”. Erzincan University Journal of Science and Technology 12 (3): 1705-14. https://doi.org/10.18185/erzifbed.653429.
EndNote
Gurdal Y (December 1, 2019) Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks. Erzincan University Journal of Science and Technology 12 3 1705–1714.
IEEE
[1]Y. Gurdal, “Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks”, Erzincan University Journal of Science and Technology, vol. 12, no. 3, pp. 1705–1714, Dec. 2019, doi: 10.18185/erzifbed.653429.
ISNAD
Gurdal, Yeliz. “Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks”. Erzincan University Journal of Science and Technology 12/3 (December 1, 2019): 1705-1714. https://doi.org/10.18185/erzifbed.653429.
JAMA
1.Gurdal Y. Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks. Erzincan University Journal of Science and Technology. 2019;12:1705–1714.
MLA
Gurdal, Yeliz. “Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks”. Erzincan University Journal of Science and Technology, vol. 12, no. 3, Dec. 2019, pp. 1705-14, doi:10.18185/erzifbed.653429.
Vancouver
1.Yeliz Gurdal. Grand Canonical Monte Carlo Modeling of Anesthetic Xe Separation from Exhale Gas Mixtures Using Metal Organic Frameworks. Erzincan University Journal of Science and Technology. 2019 Dec. 1;12(3):1705-14. doi:10.18185/erzifbed.653429

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