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, 13 (5)
doi: 10.3390/membranes13050526
Article, Bibliographie
This is the latest version of this item.
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).
Item Type: | Article |
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Erschienen: | 2023 |
Creators: | Abdollahi, Seyyed Amirreza ; Andarkhor, AmirReza ; Pourahmad, Afham ; Alibak, Ali Hosin ; Alobaid, Falah ; Aghel, Babak |
Type of entry: | Bibliographie |
Title: | Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks |
Language: | English |
Date: | 2023 |
Place of Publication: | Darmstadt |
Publisher: | MDPI |
Journal or Publication Title: | Membranes |
Volume of the journal: | 13 |
Issue Number: | 5 |
Collation: | 14 Seiten |
DOI: | 10.3390/membranes13050526 |
Corresponding Links: | |
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). |
Uncontrolled Keywords: | CO₂/CH₄ gas mixture, membrane separation, selectivity, intelligent modeling |
Additional Information: | This article belongs to the Special Issue Advances in Membrane Technology for Environmental Protection/Remediation |
Classification DDC: | 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: | 02 Aug 2024 12:52 |
Last Modified: | 02 Aug 2024 12:52 |
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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)
- Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks. (deposited 02 Aug 2024 12:52) [Currently Displayed]
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