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Cited by 324

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Basic Research Needs for Design of Adsorptive Gas Separation Processes

Sircar, Shivaji·2006·Industrial & Engineering Chemistry Research
DOI 10.1021/ie051056a
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Abstract

Separation and purification of multicomponent gas mixtures by adsorption is an established process technology. Adsorptive process design requires accurate data on multicomponent gas adsorption equilibria, kinetics, and heats as input variables. These data often cannot be predicted by using today's models, particularly for complex practical systems where the gas mixtures have adsorbates of different sizes and polarities, and the adsorbent is energetically heterogeneous. There is a large volume of pure gas and some binary gas adsorption equilibrium and kinetic data in the published literature, but multicomponent adsorption data are rare. The data for heats of adsorption are only emerging. There is a desperate need to generate a multicomponent adsorption database for better understanding of the complex phenomenon, for seriously testing existing models, and for development of new models. Two recently developed methods called “isotope exchange technique” and “microcalorimetry for adsorption heats” are recommended for measurement of the multicomponent data. Needs to satisfy thermodynamic consistencies by binary gas equilibrium data and the equilibrium models are emphasized.

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Metadata

Authors
Sircar, S.
Venue
Industrial & Engineering Chemistry Research
Publisher
American Chemical Society (ACS)
Published
2006-02-03
Volume
45
Issue
16
Pages
5435-5448
ISSN
  • 0888-5885 (ISSN-L)
  • 1520-5045
DOI
10.1021/ie051056a
OpenAlex
W1999351214
Cited by
324
References
61
FWCI
3.55
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.