Membrane preparation assisted by integration of machine learning and response surface methodology for CO2 separation
Carrillo-Sánchez, Lucía (Universidad de Zaragoza) ; Téllez, Carlos (Universidad de Zaragoza) ; Coronas, Joaquín (Universidad de Zaragoza)
Resumen: The separation of carbon dioxide (CO2) is presented as a current challenge in the environment and energy sector. The primary reason for this is to control the emissions of this gas into the atmosphere and the upgrading of biomethane. In this context, the membrane separation technology seems to be a very sustainable promising tool for such tasks. This work presents a machine learning (ML) study, based on a database created from membrane preparation conditions and gas separation records from the literature, achieved for the CO2/N2 and CO2/CH4 mixtures using dense membranes of thermoplastic elastomer Pebax® 1657. A comparative analysis of three different ML models was carried out: multiple linear regression, decision tree and random forest. This last algorithm demonstrates the best performance in statistics terms of coefficient of determination and root mean square error. In addition, the combination of the ML random forest with a method based on the design of experiments with response surface methodology (RSM) allowed to identify the favorable conditions for the membrane synthesis, with the objective of enhancing the CO2 separation performance. This resulted in prepared membranes in the laboratory considering the proposed conditions by RSM with CO2 permeability and CO2/X selectivity values of 115 Barrer and 43.5 and 132 Barrer and 16.4 for the CO2/N2 and CO2/CH4 mixtures, respectively, at 35 °C.
Idioma: Inglés
DOI: 10.1016/j.memsci.2025.124708
Año: 2025
Publicado en: JOURNAL OF MEMBRANE SCIENCE 736 (2025), 124708 [12 pp.]
ISSN: 0376-7388
Financiación: info:eu-repo/grantAgreement/ES/DGA/T68-23R
Financiación: info:eu-repo/grantAgreement/ES/MICIU/CEX2023-001286-S
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PID2022-138582OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PRTR-C16.R1
Tipo y forma: Article (Published version)
Área (Departamento): Área Química Analítica (Dpto. Química Analítica)
Área (Departamento): Área Ingeniería Química (Dpto. Ing.Quím.Tecnol.Med.Amb.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2025-10-09-13:25:56)
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Idioma: Inglés
DOI: 10.1016/j.memsci.2025.124708
Año: 2025
Publicado en: JOURNAL OF MEMBRANE SCIENCE 736 (2025), 124708 [12 pp.]
ISSN: 0376-7388
Financiación: info:eu-repo/grantAgreement/ES/DGA/T68-23R
Financiación: info:eu-repo/grantAgreement/ES/MICIU/CEX2023-001286-S
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PID2022-138582OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PRTR-C16.R1
Tipo y forma: Article (Published version)
Área (Departamento): Área Química Analítica (Dpto. Química Analítica)
Área (Departamento): Área Ingeniería Química (Dpto. Ing.Quím.Tecnol.Med.Amb.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2025-10-09-13:25:56)
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Articles > Artículos por área > Ingeniería Química
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Record created 2025-10-02, last modified 2025-10-09