Data-driven future for nanofiltration: Escaping linearity

Ignácz, Gergő; Beke, Aron K.; Székely, György

doi:10.1016/j.memlet.2023.100040

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Exploit Articial Intelligence1

Data Structure: Descriptors Of the Input Space1

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2024

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Cited by 5 publications

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“…Specic information on data-driven membrane technology for instance can be found in the literature. [26][27][28]…”

Section: Exploit Articial Intelligencementioning

confidence: 99%

The 12 principles of green membrane materials and processes for realizing the United Nations' sustainable development goals

Szekely

2024

RSC Sustain.

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“…Specic information on data-driven membrane technology for instance can be found in the literature. [26][27][28]…”

Section: Exploit Articial Intelligencementioning

confidence: 99%

The 12 principles of green membrane materials and processes for realizing the United Nations' sustainable development goals

Szekely

2024

RSC Sustain.

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“…One could imagine instead more abstract theoretical molecular descriptors, calculated at the molecular level. These can be built from descriptors as simple as the number of atoms, their type, coordinates, bonds and electrostatic interactions, to 2D molecular graphs, or even based on calculations from density functional theory (dft) [47]. A popular technique for finding such theoretical molecular descriptors are QSAR (quantitative structure-activity relationship) models.…”

Section: Data Structure: Descriptors Of the Input Spacementioning

confidence: 99%

Organic Solvent Nanofiltration and Data-Driven Approaches

Piccard,

Borges,

Cleuren

et al. 2023

Separations

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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.

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