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A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources

[Image: see text] The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO(2) capture processes. This is particularly of interest in novel, diluted, and...

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Autores principales: Grimm, Alexa, Gazzani, Matteo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501812/
https://www.ncbi.nlm.nih.gov/pubmed/36164596
http://dx.doi.org/10.1021/acs.iecr.2c01695
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author Grimm, Alexa
Gazzani, Matteo
author_facet Grimm, Alexa
Gazzani, Matteo
author_sort Grimm, Alexa
collection PubMed
description [Image: see text] The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO(2) capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO(2) removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, y(CO(2)) = 0.1%, and y(CO(2)) = 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.
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spelling pubmed-95018122022-09-24 A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources Grimm, Alexa Gazzani, Matteo Ind Eng Chem Res [Image: see text] The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO(2) capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO(2) removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, y(CO(2)) = 0.1%, and y(CO(2)) = 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D. American Chemical Society 2022-09-06 2022-09-21 /pmc/articles/PMC9501812/ /pubmed/36164596 http://dx.doi.org/10.1021/acs.iecr.2c01695 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Grimm, Alexa
Gazzani, Matteo
A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title_full A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title_fullStr A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title_full_unstemmed A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title_short A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO(2) Capture from Diluted Sources
title_sort machine learning-aided equilibrium model of vtsa processes for sorbents screening applied to co(2) capture from diluted sources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501812/
https://www.ncbi.nlm.nih.gov/pubmed/36164596
http://dx.doi.org/10.1021/acs.iecr.2c01695
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