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Stochastic modelling of membrane filtration

Abstract:

Membrane fouling during particle filtration occurs through a variety of mechanisms, including internal pore clogging by contaminants, coverage of pore entrances, and deposition on the membrane surface. In this paper we present an efficient method for modelling the behaviour of a filter, which accounts for different retention mechanisms, particle sizes, and membrane geometries. The membrane is assumed to be composed of a series of, possibly interconnected, pores.


The central feature is a conductivity function, which describes the blockage of each individual pore as particles arrive, which is coupled with a mechanism to account for the stochastic nature of the arrival times of particles at the pore. The result is a system of ordinary differential equations based on the pore-level interactions.


We demonstrate how our model can accurately describe a wide range of filtration scenarios. Specifically, we consider: a case where blocking via multiple mechanisms can occur simultaneously, which have previously required the study through individual models; the filtration of a combination of small and large particles by a track-etched membrane; and particle separation using interconnected pore networks. The model is significantly faster than comparable stochastic simulations for small networks, enabling its use as a tool for efficient future simulations.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rspa.2016.0948

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author


Publisher:
Royal Society
Journal:
Proceedings of the Royal Society A More from this journal
Volume:
473
Pages:
20160948
Publication date:
2017-04-01
Acceptance date:
2017-03-21
DOI:


Keywords:
Subjects:
Pubs id:
pubs:688916
UUID:
uuid:75b51ea2-7b37-4665-8fcd-7d38a498b7de
Local pid:
pubs:688916
Source identifiers:
688916
Deposit date:
2017-04-10
ARK identifier:

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