4.4 Article

High-temperature CO2 removal from CH4 using silica membrane: experimental and neural network modeling

期刊

GREENHOUSE GASES-SCIENCE AND TECHNOLOGY
卷 9, 期 5, 页码 1010-1026

出版社

WILEY PERIODICALS, INC
DOI: 10.1002/ghg.1916

关键词

alumina; silica membrane; sol-gel method; permselectivity; dip coating; CO2 and CH4 separation; artificial neural network modeling; feedforward back-propagation neural network

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Inorganic membranes can operate under harsh conditions. However, successful synthesis of inorganic membranes is still challenging, and its performance depends on many factors. This work reports the effect of dip-coating duration, inlet pressure, and inlet flow rate on the flux, permeability, and selectivity of silica membranes. A silica membrane was prepared by the deposition of silica sol onto porous alumina support. The permeability test was conducted at 100 degrees C using a single gas of CO2 and CH4. The highest flux was observed at the maximum inlet pressure and inlet flow rate for the membrane prepared at the minimum dip-coating duration. The neural network modeling of the membrane predicted permeabilities showed a considerably high validity regression (R approximate to 0.99) of the predicted data linked to the experimental sets. The separation factor (alpha) was the highest at the maximum dip-coating duration. The synthesized silica membrane has potential for CO2/CH4 separation under harsh operating conditions. (c) 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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