Data-driven predictions of complex organic mixture permeation in polymer membranes
Abstract Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic m...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40257-2 |
| _version_ | 1797558403416981504 |
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| author | Young Joo Lee Lihua Chen Janhavi Nistane Hye Youn Jang Dylan J. Weber Joseph K. Scott Neel D. Rangnekar Bennett D. Marshall Wenjun Li J. R. Johnson Nicholas C. Bruno M. G. Finn Rampi Ramprasad Ryan P. Lively |
| author_facet | Young Joo Lee Lihua Chen Janhavi Nistane Hye Youn Jang Dylan J. Weber Joseph K. Scott Neel D. Rangnekar Bennett D. Marshall Wenjun Li J. R. Johnson Nicholas C. Bruno M. G. Finn Rampi Ramprasad Ryan P. Lively |
| author_sort | Young Joo Lee |
| collection | DOAJ |
| description | Abstract Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures. |
| first_indexed | 2024-03-10T17:29:59Z |
| format | Article |
| id | doaj.art-babb256d06bd41cf8c0d517a183e3f76 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:29:59Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-babb256d06bd41cf8c0d517a183e3f762023-11-20T10:03:24ZengNature PortfolioNature Communications2041-17232023-08-0114111210.1038/s41467-023-40257-2Data-driven predictions of complex organic mixture permeation in polymer membranesYoung Joo Lee0Lihua Chen1Janhavi Nistane2Hye Youn Jang3Dylan J. Weber4Joseph K. Scott5Neel D. Rangnekar6Bennett D. Marshall7Wenjun Li8J. R. Johnson9Nicholas C. Bruno10M. G. Finn11Rampi Ramprasad12Ryan P. Lively13School of Chemical and Biomolecular Engineering, Georgia Institute of TechnologySchool of Materials Science and Engineering, Georgia Institute of TechnologySchool of Materials Science and Engineering, Georgia Institute of TechnologySchool of Chemical and Biomolecular Engineering, Georgia Institute of TechnologySchool of Chemical and Biomolecular Engineering, Georgia Institute of TechnologySchool of Chemical and Biomolecular Engineering, Georgia Institute of TechnologyExxonMobil Technology and Engineering CompanyExxonMobil Technology and Engineering CompanyExxonMobil Technology and Engineering CompanyExxonMobil Technology and Engineering CompanySchool of Chemistry and Biochemistry, Georgia Institute of TechnologySchool of Chemistry and Biochemistry, Georgia Institute of TechnologySchool of Materials Science and Engineering, Georgia Institute of TechnologySchool of Chemical and Biomolecular Engineering, Georgia Institute of TechnologyAbstract Membrane-based organic solvent separations are rapidly emerging as a promising class of technologies for enhancing the energy efficiency of existing separation and purification systems. Polymeric membranes have shown promise in the fractionation or splitting of complex mixtures of organic molecules such as crude oil. Determining the separation performance of a polymer membrane when challenged with a complex mixture has thus far occurred in an ad hoc manner, and methods to predict the performance based on mixture composition and polymer chemistry are unavailable. Here, we combine physics-informed machine learning algorithms (ML) and mass transport simulations to create an integrated predictive model for the separation of complex mixtures containing up to 400 components via any arbitrary linear polymer membrane. We experimentally demonstrate the effectiveness of the model by predicting the separation of two crude oils within 6-7% of the measurements. Integration of ML predictors of diffusion and sorption properties of molecules with transport simulators enables for the rapid screening of polymer membranes prior to physical experimentation for the separation of complex liquid mixtures.https://doi.org/10.1038/s41467-023-40257-2 |
| spellingShingle | Young Joo Lee Lihua Chen Janhavi Nistane Hye Youn Jang Dylan J. Weber Joseph K. Scott Neel D. Rangnekar Bennett D. Marshall Wenjun Li J. R. Johnson Nicholas C. Bruno M. G. Finn Rampi Ramprasad Ryan P. Lively Data-driven predictions of complex organic mixture permeation in polymer membranes Nature Communications |
| title | Data-driven predictions of complex organic mixture permeation in polymer membranes |
| title_full | Data-driven predictions of complex organic mixture permeation in polymer membranes |
| title_fullStr | Data-driven predictions of complex organic mixture permeation in polymer membranes |
| title_full_unstemmed | Data-driven predictions of complex organic mixture permeation in polymer membranes |
| title_short | Data-driven predictions of complex organic mixture permeation in polymer membranes |
| title_sort | data driven predictions of complex organic mixture permeation in polymer membranes |
| url | https://doi.org/10.1038/s41467-023-40257-2 |
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