Modeling the CO 2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks.

Autor: Abdollahi SA; Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran. s.a_abdollahi@yahoo.com., Ranjbar SF; Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 May 31; Vol. 13 (1), pp. 8812. Date of Electronic Publication: 2023 May 31.
DOI: 10.1038/s41598-023-36071-x
Abstrakt: Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO 2 ), which is an environmental pollutant with a greenhouse effect. The CO 2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO 2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO 2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO 2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO 2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO 2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO 2 permeability in PMP/ZnO, PMP/Al 2 O 3 , PMP/TiO 2 , and PMP/TiO 2 -NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO 2 -NT (functionalized nanotube with titanium dioxide) is the best medium for CO 2 separation.
(© 2023. The Author(s).)
Databáze: MEDLINE
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