Understanding the performance of RHO type zeolite membrane for CH 4 /N 2 separation based on molecular dynamics and deep neural network methods.

Autor: Ghasemi F; Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran., Alizadeh M; Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran., Azamat J; Department of Chemistry Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran., Erfan-Niya H; Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran. Electronic address: herfan@tabrizu.ac.ir.
Jazyk: angličtina
Zdroj: Journal of molecular graphics & modelling [J Mol Graph Model] 2024 Mar; Vol. 127, pp. 108673. Date of Electronic Publication: 2023 Nov 17.
DOI: 10.1016/j.jmgm.2023.108673
Abstrakt: This study shows a molecular dynamics (MD) simulation study on the performance of the RHO zeolite membrane for separating nitrogen from methane/nitrogen gas mixtures. The contamination of natural gas, predominantly composed of methane, with nitrogen diminishes its value. Zeolite membranes offer promising prospects for gas separation due to their stability, rigid pore structure, and molecular sieving properties. The study investigates the impact of pressure difference (up to 30 MPa), feed composition, and membrane thickness on the separation rate at a system temperature of 298 K. Results demonstrate that the RHO zeolite membrane exhibits high permeability and selectivity for N 2 separation, surpassing the upper limit defined by Robson with a maximum permeability of 2.14 × 10 5 GPU (Gas Permeation Units). Exceptional selectivity of N 2 over CH 4 molecules is observed. Additionally, altering the feed composition and membrane thickness positively influences the membrane's separation performance, thereby enhancing its efficiency. The findings contribute to the advancement of separation technologies, providing valuable insights into the potential application of zeolite membranes for efficient N 2 separation from CH 4 /N 2 gas mixtures in natural gas processing. Furthermore, the study explores the use of Deep Neural Network (DNN) models to predict the membrane's performance under diverse operating conditions. The DNN models, trained using simulation data from MD simulations, exhibit high accuracy with a coefficient of determination (R 2 ) exceeding 0.9, ensuring reliable predictions. The integration of DNN models facilitates the optimization of zeolite membrane-based gas separation systems, improving their design and operation.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: