Organic Solvent Nanofiltration and Data-Driven Approaches

Abstract

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.

Keywords

• organic solvent nanofiltration
• data science
• mathematical modeling
• machine learning
• data standardization