602269 Carbon Molecular Sieve Hollow Fiber Membranes for High Purity Oxygen Production

Wednesday, November 18, 2020
Separations Division (02) (Poster Gallery)
Jong Geun Seong1, John Matteson2, Jeremy Lewis2, John Baca2, Alexander J Josephson2, Christopher Russell2, Troy Holland2, Joel Kress2, Kathryn A. Berchtold3 and Rajinder P. Singh2, (1)Materials Physics and Applications, Los Alamos National Laboratory, Los Alamos, NM, (2)Los Alamos National Laboratory, Los Alamos, NM, (3)Carbon Capture and Separations for Energy Applications (CaSEA) Labs, Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, NM

Hydrocarbon fuel combustion in oxygen enriched air (OEA) is studied as a pathway to improve energy efficiency of carbon capture process. Membrane technology is a good alternative to industry standard cryogenic methods for OEA production owing to their practical merits including continuous process operations, good scalability, and cost-effectiveness with small footprint. In particular, carbon molecular sieve (CMS) membranes have gained attention for gas separation including air separations. The gas separation performance of the CMS membranes can be controlled for specific gas pairs (e.g. O2/N2, CO2/CH4, H2/CO2) to meet application specific needs with proper polymer precursors selections and programmable pyrolysis protocols. Here, we explore CMS hollow fiber membranes (HFMs) prepared from rigid and rod-like aromatic polybenzimidazole (PBI) materials for OEA productions. PBI mambrane material highly-packed interchain configurations endowed the resulting CMS HFMs with more oxygen selective characteristics than previously reported CMS membranes derived from other polymers. Comprehensive investigations of pyrolysis protocols for highly oxygen selective PBI-based CMS HFM fabrications and their performance relationships will be discussed. In addition, practical feasiblity of energy efficient OEA productions using CMS HFMs is assessed via process modelling simulations with the selected performance data.

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