期刊
MOLECULAR SYSTEMS DESIGN & ENGINEERING
卷 5, 期 7, 页码 1205-1218出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0me00060d
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资金
- National Science Foundation [CBET-1604890]
- Northwestern University [p30624]
Many metal-organic framework (MOF) materials have been reported in the literature as promising for carbon capture applications based on isotherm data or simple adsorbent metrics. However, adsorption process conditions are often neglected in these evaluations. In this study, we performed process-level simulation and optimization of pressure swing adsorption processes on a set of promising MOFs reported in the literature for post-combustion carbon capture. Zeolite 13X was also included as a benchmark material. We examined the ability of the MOFs to achieve the Department of Energy goals of 90% CO(2)purity and 90% CO(2)recovery by employing process-level optimization using three different cycle configurations: a modified Skarstrom cycle, a five-step cycle, and a fractionated vacuum swing adsorption cycle. Then, we ranked the MOFs based on their economic performance by looking at the productivity and energy requirements for each cycle. We compared this ranking of the MOFs with the rankings provided by other metrics and found that the adsorbent rankings suggested by simplified metrics may differ significantly from the rankings predicted by detailed process optimization. The economic optimization analysis suggests that the best performing MOFs from those analyzed here are UTSA-16, Cu-TDPAT, Zn-MOF-74, Ti-MIL-91, and SIFSIX-3-Ni. Looking at the connection between process performance and material properties, we found that high CO(2)working capacity, small pore size, and large difference between the heat of adsorption of CO(2)and N(2)promote CO(2)capture ability based on this small data set. We synthesized one of the top performing MOFs, SIFSIX-3-Ni, and measured its CO(2)and N(2)adsorption isotherms. The measured isotherms allowed us to estimate the N(2)heat of adsorption for SIFSIX-3-Ni, which was not previously available and was required for the process-level modelling.
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