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
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Alibak, Ali Hosin ; Alizadeh, Seyed Mehdi ; Davodi Monjezi, Shaghayegh ; Alizadeh, As’ad ; Alobaid, Falah ; Aghel, Babak |
| Art des Eintrags: | Bibliographie |
| Titel: | Developing a hybrid neuro-fuzzy method to predict Carbon Dioxide (CO₂) permeability in mixed matrix membranes containing SAPO-34 zeolite |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Membranes |
| Jahrgang/Volume einer Zeitschrift: | 12 |
| (Heft-)Nummer: | 11 |
| Kollation: | 15 Seiten |
| DOI: | 10.3390/membranes12111147 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | mixed matrix membrane, SAPO-34 zeolite, carbon dioxide separation, theoretical analysis, adaptive neuro-fuzzy inference system (ANFIS) |
| Zusätzliche Informationen: | This article belongs to the Special Issue Advances in Membrane Technology for Environmental Protection/Remediation |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau 600 Technik, Medizin, angewandte Wissenschaften > 660 Technische Chemie |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Energiesysteme und Energietechnik (EST) |
| Hinterlegungsdatum: | 15 Jan 2024 07:22 |
| Letzte Änderung: | 16 Jan 2024 09:47 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO₂) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite. (deposited 12 Jan 2024 13:35)
- Developing a hybrid neuro-fuzzy method to predict Carbon Dioxide (CO₂) permeability in mixed matrix membranes containing SAPO-34 zeolite. (deposited 15 Jan 2024 07:22) [Gegenwärtig angezeigt]
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