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Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks

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
Article, Secondary publication, Publisher's Version

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
Erschienen: 2023
Creators: Abdollahi, Seyyed Amirreza ; Andarkhor, AmirReza ; Pourahmad, Afham ; Alibak, Ali Hosin ; Alobaid, Falah ; Aghel, Babak
Type of entry: Secondary publication
Title: Simulating and Comparing CO₂/CH₄ Separation Performance of Membrane–Zeolite Contactors by Cascade Neural Networks
Language: English
Date: 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Publisher: MDPI
Journal or Publication Title: Membranes
Volume of the journal: 13
Issue Number: 5
Collation: 14 Seiten
DOI: 10.26083/tuprints-00024092
URL / URN: https://tuprints.ulb.tu-darmstadt.de/24092
Corresponding Links:
Origin: Secondary publication DeepGreen
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
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-240924
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: 19 Jun 2023 13:08
Last Modified: 20 Jun 2023 06:01
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