Enantioselective nanofiltration using predictive process modeling: Bridging the gap between materials development and process requirements

Abstract
Organic solvent nanofiltration (OSN) is a low-energy alternative for continuous separations in the chemical industry. As the pharmaceutical sector increasingly turns toward continuous manufacturing, OSN could become a sustainable solution for chiral separations. Here we present the first comprehensive theoretical assessment of enantioselective OSN processes. Lumped dynamic models were developed for various system configurations, including structurally diverse nanofiltration cascades and single-stage separations with side-stream recycling and in situ racemization. Enantiomer excess and recovery characteristics of the different processes were assessed in terms of the solute rejection values of the enantiomer pairs. The general feasibility of stereochemical resolution using OSN processes is discussed in detail. Fundamental connections between rejection selectivity, permeance selectivity, and enantiomer excess limitations are revealed. Quantitative process performance examples are presented based on theoretical rejection scenarios and cases from the literature on chiral membranes. A model-based prediction tool can be found on www.osndatabase.com/enantioseparation to aid researchers in connecting materials development results with early-stage process performance assessments.

Citation
Beke, A. K., & Szekely, G. (2022). Enantioselective nanofiltration using predictive process modeling: Bridging the gap between materials development and process requirements. Journal of Membrane Science, 121020. https://doi.org/10.1016/j.memsci.2022.121020

Acknowledgements
The authors wish to thank Gergo Ignacz for his contribution to implementing the online modeling tool, and for his insights and useful discussions on the manuscript. This study was funded by the King Abdullah University of Science and Technology (KAUST). The graphical abstract and process schemes were produced by Ana Bigio, a scientific illustrator at KAUST.

Publisher
Elsevier BV

Journal
Journal of Membrane Science

DOI
10.1016/j.memsci.2022.121020

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0376738822007657

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