Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture-Reference-Cited by-同舟云学术

Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture

Published:2019-01-18 Issue:7 Volume:123 Page:4133-4139
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Dureckova Hana¹,Krykunov Mykhaylo¹,Aghaji Mohammad Zein¹,Woo Tom K.¹^ORCID

Affiliation:

1. Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa K1N 6N5, Canada

Funder

University of Ottawa

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.8b10644

Reference29 articles.

1. A review of efficiency penalty in a coal-fired power plant with post-combustion CO2 capture

2. Pachauri, R. K.; Allen, M. R.; Barros, V. R.; Broome, J.; Cramer, W.; Christ, R.; Church, J. A.; Clarke, L.; Dahe, Q.; Dasgupta, P. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Pachauri, R. K., Meyer, L., Eds. Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2014.

3. Carbon Dioxide Capture in Metal–Organic Frameworks

4. Metal−Organic Frameworks as Adsorbents for Hydrogen Purification and Precombustion Carbon Dioxide Capture

5. Baseline Flowsheet Model for IGCC with Carbon Capture

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