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Lee YJ, Chen L, Nistane J, Jang HY, Weber DJ, Scott JK, Rangnekar ND, Marshall BD, Li W, Johnson JR, Bruno NC, Finn MG, Ramprasad R, Lively RP. Data-driven predictions of complex organic mixture permeation in polymer membranes. Nat Commun 2023; 14:4931. [PMID: 37582784 PMCID: PMC10427679 DOI: 10.1038/s41467-023-40257-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/17/2023] [Indexed: 08/17/2023] Open
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.
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Affiliation(s)
- Young Joo Lee
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Lihua Chen
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Janhavi Nistane
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hye Youn Jang
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Dylan J Weber
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Joseph K Scott
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Neel D Rangnekar
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Bennett D Marshall
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Wenjun Li
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - J R Johnson
- ExxonMobil Technology and Engineering Company, Annandale, NJ, 08801, USA
| | - Nicholas C Bruno
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - M G Finn
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Rampi Ramprasad
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Ryan P Lively
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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