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

Ullah, S. and Assiri, M.A. and Al-Sehemi, A.G. and Bustam, M.A. and Abdul Mannan, H. and Abdulkareem, F.A. and Irfan, A. and Saqib, S. (2019) High-temperature CO2 removal from CH4 using silica membrane: experimental and neural network modeling. Greenhouse Gases: Science and Technology, 9 (5). pp. 1010-1026. ISSN 21523878

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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 °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 � 0.99) of the predicted data linked to the experimental sets. The separation factor (α) was the highest at the maximum dip-coating duration. The synthesized silica membrane has potential for CO2/CH4 separation under harsh operating conditions. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

Item Type: Article
Additional Information: cited By 24
Uncontrolled Keywords: Alumina; Aluminum oxide; Backpropagation; Carbon dioxide; Chemical industry; Coatings; Gas permeable membranes; Inlet flow; Silica; Sols, Artificial neural network modeling; Dip coating; Feed-forward back-propagation neural networks; Gel method; Permselectivities; Silica membrane, Neural networks, artificial neural network; back propagation; carbon dioxide; experimental study; high temperature; membrane; methane; separation
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:25
Last Modified: 10 Nov 2023 03:25
URI: https://khub.utp.edu.my/scholars/id/eprint/11278

Actions (login required)

View Item
View Item