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

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...

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Autores principales: Abdollahi, Seyyed Amirreza, Ranjbar, Seyyed Faramarz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232494/
https://www.ncbi.nlm.nih.gov/pubmed/37258709
http://dx.doi.org/10.1038/s41598-023-36071-x
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author Abdollahi, Seyyed Amirreza
Ranjbar, Seyyed Faramarz
author_facet Abdollahi, Seyyed Amirreza
Ranjbar, Seyyed Faramarz
author_sort Abdollahi, Seyyed Amirreza
collection PubMed
description 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.
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spelling pubmed-102324942023-06-02 Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks Abdollahi, Seyyed Amirreza Ranjbar, Seyyed Faramarz Sci Rep Article 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. Nature Publishing Group UK 2023-05-31 /pmc/articles/PMC10232494/ /pubmed/37258709 http://dx.doi.org/10.1038/s41598-023-36071-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Abdollahi, Seyyed Amirreza
Ranjbar, Seyyed Faramarz
Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title_full Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title_fullStr Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title_full_unstemmed Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title_short Modeling the CO(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
title_sort modeling the co(2) separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232494/
https://www.ncbi.nlm.nih.gov/pubmed/37258709
http://dx.doi.org/10.1038/s41598-023-36071-x
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