Journal
CHEMICAL ENGINEERING SCIENCE
Volume 230, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2020.116214
Keywords
Perovskite membrane; Hollow fiber module; Oxygen separation; Computational fluid dynamic (CFD) analysis
Categories
Funding
- National Natural Science Foundation of China [91745116, 21805206]
- Tianjin Research Program of Application Foundation and Advanced Technology [17JCZDJC36900]
- Science and Technology Plans of Tianjin [16PTSYJC00090]
- Ministry of Education in Singapore (MOE) [WBS: R279-000544-112]
- Singapore Agency for Science, Technology and Research (A*STAR) AME IRG grant [A1783c0016]
- National Environment Agency (NEA) in Singapore [WTE-CRP 1501-103]
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A 3D model based on the computational fluid dynamic method is developed to investigate perovskite hollow fiber membrane modules for oxygen separation. Increasing the vacuum level on the permeate side is more effective in improving oxygen separation rate, while preheating the air feed also significantly enhances the module's oxygen separation performance.
A 3D model is developed based on the computational fluid dynamic (CFD) method to investigate the behavior of perovskite hollow fiber membrane modules for oxygen separation. The User Defined Functions (UDFs) and FLUENT software are used to calculate simulation results and validated by the experimental data from a La0.6Sr0.4Co0.2Fe0.8O3-d (LSCF) membrane module, which was assembled with 7 hollow fiber membranes. The effect of operating conditions such as preheating air feed, pressurizing air feed, and/or vacuuming has been investigated based on the oxygen permeation rate, temperature, oxygen concentration and gas velocity distribution in the membrane module. The simulation results indicate that increasing the vacuum level on the permeate side is far more effective to improve the oxygen separation rate than increasing the pressure on the air feed side. Preheating the air feed with the air effluent or the oxygen product also noticeably enhances the oxygen separation performance of the module. (C) 2020 Published by Elsevier Ltd.
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