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Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO(2)) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, press...

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Autores principales: Alibak, Ali Hosin, Alizadeh, Seyed Mehdi, Davodi Monjezi, Shaghayegh, Alizadeh, As’ad, Alobaid, Falah, Aghel, Babak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699495/
https://www.ncbi.nlm.nih.gov/pubmed/36422139
http://dx.doi.org/10.3390/membranes12111147
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author Alibak, Ali Hosin
Alizadeh, Seyed Mehdi
Davodi Monjezi, Shaghayegh
Alizadeh, As’ad
Alobaid, Falah
Aghel, Babak
author_facet Alibak, Ali Hosin
Alizadeh, Seyed Mehdi
Davodi Monjezi, Shaghayegh
Alizadeh, As’ad
Alobaid, Falah
Aghel, Babak
author_sort Alibak, Ali Hosin
collection PubMed
description This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO(2)) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO(2) permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO(2) separation.
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spelling pubmed-96994952022-11-26 Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite Alibak, Ali Hosin Alizadeh, Seyed Mehdi Davodi Monjezi, Shaghayegh Alizadeh, As’ad Alobaid, Falah Aghel, Babak Membranes (Basel) Article This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO(2)) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO(2) permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO(2) separation. MDPI 2022-11-16 /pmc/articles/PMC9699495/ /pubmed/36422139 http://dx.doi.org/10.3390/membranes12111147 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alibak, Ali Hosin
Alizadeh, Seyed Mehdi
Davodi Monjezi, Shaghayegh
Alizadeh, As’ad
Alobaid, Falah
Aghel, Babak
Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title_full Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title_fullStr Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title_full_unstemmed Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title_short Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO(2)) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite
title_sort developing a hybrid neuro-fuzzy method to predict carbon dioxide (co(2)) permeability in mixed matrix membranes containing sapo-34 zeolite
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699495/
https://www.ncbi.nlm.nih.gov/pubmed/36422139
http://dx.doi.org/10.3390/membranes12111147
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