Volume 58, Issue 7 pp. 1892-1902
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Membrane Filtration with Liquids: A Global Approach with Prior Successes, New Developments and Unresolved Challenges

Dr. Georges Belfort

Corresponding Author

Dr. Georges Belfort

Howard P. Isermann Department of Chemical and Biological Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180-3590 USA

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First published: 29 October 2018
Citations: 50

Graphical Abstract

Energy-consuming thermal separation processes such as distillation will eventually be replaced by low energy-consuming processes like membrane filtration. This change has already occurred for desalination of seawater. Major challenges, however, remain with respect to increasing selectivity and permeation flux and controlling mass transport. New predictive models are needed that account for all “three legs”—selectivity, capacity and transport of mass and momentum.

Abstract

After 70 years, modern pressure-driven polymer membrane processes with liquids are mature and accepted in many industries due to their good performance, ease of scale-up, low energy consumption, modular compact construction, and low operating costs compared with thermal systems. Successful isothermal operation of synthetic membranes with liquids requires consideration of three critical aspects or “legs” in order of relevance: selectivity, capacity (i.e. permeation flow rate per unit area) and transport of mass and momentum comprising concentration polarization (CP) and fouling (F). Major challenges remain with respect to increasing selectivity and controlling mass transport in, to and away from membranes. Thus, prediction and control of membrane morphology and a deep understanding of the mechanism of dissolved and suspended solute transport near and in the membrane (i.e. diffusional and convective mass transport) is essential. Here, we focus on materials development to address the relatively poor selectivity of liquid membrane filtration with polymers and discuss the critical aspects of transport limitations. Machine learning could help optimize membrane structure design and transport conditions for improved membrane filtration performance.

Conflict of interest

The author declares no conflict of interest.

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