Literature DB >> 25042380

A combined network model for membrane fouling.

I M Griffiths1, A Kumar2, P S Stewart3.   

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. Each of these fouling mechanisms results in a decline in the observed flow rate over time, and the decrease in filtration efficiency can be characterized by a unique signature formed by plotting the volumetric flux, Q^, as a function of the total volume of fluid processed, V^. When membrane fouling takes place via any one of these mechanisms independently the Q^V^ signature is always convex downwards for filtration under a constant transmembrane pressure. However, in many such filtration scenarios, the fouling mechanisms are inherently coupled and the resulting signature is more difficult to interpret. For instance, blocking of a pore entrance will be exacerbated by the internal clogging of a pore, while the deposition of a layer of contaminants is more likely once the pores have been covered by particulates. As a result, the experimentally observed Q^V^ signature can vary dramatically from the canonical convex-downwards graph, revealing features that are not captured by existing continuum models. In a range of industrially relevant cases we observe a concave-downwardsQ^V^ signature, indicative of a fouling rate that becomes more severe with time. We derive a network model for membrane fouling that accounts for the inter-relation between fouling mechanisms and demonstrate the impact on the Q^V^ signature. Our formulation recovers the behaviour of existing models when the mechanisms are treated independently, but also elucidates the concave-downward Q^V^ signature for multiple interactive fouling mechanisms. The resulting model enables post-experiment analysis to identify the dominant fouling modality at each stage, and is able to provide insight into selecting appropriate operating regimes.
Copyright © 2014 Elsevier Inc. All rights reserved.

Keywords:  Caking; Filtration; Mathematical modelling; Membrane fouling; Network model; Pore clogging

Year:  2014        PMID: 25042380     DOI: 10.1016/j.jcis.2014.06.021

Source DB:  PubMed          Journal:  J Colloid Interface Sci        ISSN: 0021-9797            Impact factor:   8.128


  5 in total

Review 1.  Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities.

Authors:  Félix Schmitt; Khac-Uan Do
Journal:  Environ Sci Pollut Res Int       Date:  2017-09-04       Impact factor: 4.223

2.  Transition-state theory predicts clogging at the microscale.

Authors:  T van de Laar; S Ten Klooster; K Schroën; J Sprakel
Journal:  Sci Rep       Date:  2016-06-22       Impact factor: 4.379

3.  Stochastic modelling of membrane filtration.

Authors:  A U Krupp; I M Griffiths; C P Please
Journal:  Proc Math Phys Eng Sci       Date:  2017-04-26       Impact factor: 2.704

Review 4.  Recent Advances in the Prediction of Fouling in Membrane Bioreactors.

Authors:  Yaoke Shi; Zhiwen Wang; Xianjun Du; Bin Gong; Veeriah Jegatheesan; Izaz Ul Haq
Journal:  Membranes (Basel)       Date:  2021-05-24

5.  From cooperative to uncorrelated clogging in cross-flow microfluidic membranes.

Authors:  R van Zwieten; T van de Laar; J Sprakel; K Schroën
Journal:  Sci Rep       Date:  2018-04-09       Impact factor: 4.379

  5 in total

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