Organic Solvent Nanofiltration and Data-Driven Approaches
Organic solvent nanofiltration (OSN) is a membrane separation method that has gained much interest due to its promising ability to offer an energy-lean alternative for traditional thermal separation methods. Industrial acceptance, however, is held back by the slow process of membrane screening based...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Separations |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2297-8739/10/9/516 |
| _version_ | 1797576909029113856 |
|---|---|
| author | Pieter-Jan Piccard Pedro Borges Bart Cleuren Jef Hooyberghs Anita Buekenhoudt |
| author_facet | Pieter-Jan Piccard Pedro Borges Bart Cleuren Jef Hooyberghs Anita Buekenhoudt |
| author_sort | Pieter-Jan Piccard |
| collection | DOAJ |
| description | Organic solvent nanofiltration (OSN) is a membrane separation method that has gained much interest due to its promising ability to offer an energy-lean alternative for traditional thermal separation methods. Industrial acceptance, however, is held back by the slow process of membrane screening based on trial and error for each solute-solvent couple to be separated. Such time-consuming screening is necessary due to the absence of predictive models, caused by a lack of fundamental understanding of the complex separation mechanism complicated by the wide variety of solute and solvent properties, and the importance of all mutual solute-solvent-membrane affinities and competing interactions. Recently, data-driven approaches have gained a lot of attention due to their unprecedented predictive power, significantly outperforming traditional mechanistic models. In this review, we give an overview of both mechanistic models and the recent advances in data-driven modeling. In addition to other reviews, we want to emphasize the coherence of all mechanistic models and discuss their relevance in an increasingly data-driven field. We reflect on the use of data in the field of OSN and its compliance with the FAIR principles, and we give an overview of the state of the art of data-driven models in OSN. The review can serve as inspiration for any further modeling activities, both mechanistic and data-driven, in the field. |
| first_indexed | 2024-03-10T22:00:30Z |
| format | Article |
| id | doaj.art-7afb972f122e4f4e9c085d253cf5811e |
| institution | Directory Open Access Journal |
| issn | 2297-8739 |
| language | English |
| last_indexed | 2024-03-10T22:00:30Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Separations |
| spelling | doaj.art-7afb972f122e4f4e9c085d253cf5811e2023-11-19T12:58:14ZengMDPI AGSeparations2297-87392023-09-0110951610.3390/separations10090516Organic Solvent Nanofiltration and Data-Driven ApproachesPieter-Jan Piccard0Pedro Borges1Bart Cleuren2Jef Hooyberghs3Anita Buekenhoudt4Theory Lab., Faculty of Sciences, UHasselt—Hasselt University, Agoralaan, 3590 Diepenbeek, BelgiumUnit Separation and Conversion Technology, VITO N.V.—Flemish Institute of Technological Research, Boeretang 200, 2400 Mol, BelgiumTheory Lab., Faculty of Sciences, UHasselt—Hasselt University, Agoralaan, 3590 Diepenbeek, BelgiumTheory Lab., Faculty of Sciences, UHasselt—Hasselt University, Agoralaan, 3590 Diepenbeek, BelgiumUnit Separation and Conversion Technology, VITO N.V.—Flemish Institute of Technological Research, Boeretang 200, 2400 Mol, BelgiumOrganic solvent nanofiltration (OSN) is a membrane separation method that has gained much interest due to its promising ability to offer an energy-lean alternative for traditional thermal separation methods. Industrial acceptance, however, is held back by the slow process of membrane screening based on trial and error for each solute-solvent couple to be separated. Such time-consuming screening is necessary due to the absence of predictive models, caused by a lack of fundamental understanding of the complex separation mechanism complicated by the wide variety of solute and solvent properties, and the importance of all mutual solute-solvent-membrane affinities and competing interactions. Recently, data-driven approaches have gained a lot of attention due to their unprecedented predictive power, significantly outperforming traditional mechanistic models. In this review, we give an overview of both mechanistic models and the recent advances in data-driven modeling. In addition to other reviews, we want to emphasize the coherence of all mechanistic models and discuss their relevance in an increasingly data-driven field. We reflect on the use of data in the field of OSN and its compliance with the FAIR principles, and we give an overview of the state of the art of data-driven models in OSN. The review can serve as inspiration for any further modeling activities, both mechanistic and data-driven, in the field.https://www.mdpi.com/2297-8739/10/9/516organic solvent nanofiltrationdata sciencemathematical modelingmachine learningdata standardization |
| spellingShingle | Pieter-Jan Piccard Pedro Borges Bart Cleuren Jef Hooyberghs Anita Buekenhoudt Organic Solvent Nanofiltration and Data-Driven Approaches Separations organic solvent nanofiltration data science mathematical modeling machine learning data standardization |
| title | Organic Solvent Nanofiltration and Data-Driven Approaches |
| title_full | Organic Solvent Nanofiltration and Data-Driven Approaches |
| title_fullStr | Organic Solvent Nanofiltration and Data-Driven Approaches |
| title_full_unstemmed | Organic Solvent Nanofiltration and Data-Driven Approaches |
| title_short | Organic Solvent Nanofiltration and Data-Driven Approaches |
| title_sort | organic solvent nanofiltration and data driven approaches |
| topic | organic solvent nanofiltration data science mathematical modeling machine learning data standardization |
| url | https://www.mdpi.com/2297-8739/10/9/516 |
| work_keys_str_mv | AT pieterjanpiccard organicsolventnanofiltrationanddatadrivenapproaches AT pedroborges organicsolventnanofiltrationanddatadrivenapproaches AT bartcleuren organicsolventnanofiltrationanddatadrivenapproaches AT jefhooyberghs organicsolventnanofiltrationanddatadrivenapproaches AT anitabuekenhoudt organicsolventnanofiltrationanddatadrivenapproaches |