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Developing a hybrid neuro-fuzzy method to predict Carbon Dioxide (CO₂) 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 (2022)
Developing a hybrid neuro-fuzzy method to predict Carbon Dioxide (CO₂) permeability in mixed matrix membranes containing SAPO-34 zeolite.
In: Membranes, 12 (11)
doi: 10.3390/membranes12111147
Article, Bibliographie

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Abstract

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO₂) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO₂ 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 CO₂ separation.

Item Type: Article
Erschienen: 2022
Creators: Alibak, Ali Hosin ; Alizadeh, Seyed Mehdi ; Davodi Monjezi, Shaghayegh ; Alizadeh, As’ad ; Alobaid, Falah ; Aghel, Babak
Type of entry: Bibliographie
Title: Developing a hybrid neuro-fuzzy method to predict Carbon Dioxide (CO₂) permeability in mixed matrix membranes containing SAPO-34 zeolite
Language: English
Date: 2022
Place of Publication: Basel
Publisher: MDPI
Journal or Publication Title: Membranes
Volume of the journal: 12
Issue Number: 11
Collation: 15 Seiten
DOI: 10.3390/membranes12111147
Corresponding Links:
Abstract:

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO₂) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO₂ 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 CO₂ separation.

Uncontrolled Keywords: mixed matrix membrane, SAPO-34 zeolite, carbon dioxide separation, theoretical analysis, adaptive neuro-fuzzy inference system (ANFIS)
Additional Information:

This article belongs to the Special Issue Advances in Membrane Technology for Environmental Protection/Remediation

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 660 Chemical engineering
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institut für Energiesysteme und Energietechnik (EST)
Date Deposited: 15 Jan 2024 07:22
Last Modified: 16 Jan 2024 09:47
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