Abdollahi, Seyyed Amirreza ; Andarkhor, AmirReza ; Pourahmad, Afham ; Alibak, Ali Hosin ; Alobaid, Falah ; Aghel, Babak (2023)
Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks.
In: Membranes, 2023, 13 (5)
doi: 10.26083/tuprints-00024092
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Abdollahi, Seyyed Amirreza ; Andarkhor, AmirReza ; Pourahmad, Afham ; Alibak, Ali Hosin ; Alobaid, Falah ; Aghel, Babak |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2023 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Membranes |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 5 |
| Kollation: | 14 Seiten |
| DOI: | 10.26083/tuprints-00024092 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/24092 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964). |
| Freie Schlagworte: | CO₂/CH₄ gas mixture, membrane separation, selectivity, intelligent modeling |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-240924 |
| 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 > 660 Technische Chemie |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Energiesysteme und Energietechnik (EST) |
| Hinterlegungsdatum: | 19 Jun 2023 13:08 |
| Letzte Änderung: | 20 Jun 2023 06:01 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks. (deposited 19 Jun 2023 13:08) [Gegenwärtig angezeigt]
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