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Discrete element modelling of filtration and backwashing processes to optimise filter design and operation

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  • Full or part time
    Prof John Bridgeman
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The primary aim of the work proposed here is to develop a comprehensive numerical model which can robustly simulate the filtration and associated air scour / backwashing processes of a granular media filter.

The use of granular media filtration is a well-established and widely practised process in water treatment. Granular media filters provide a vital role in the removal of particulate matter and in providing an effective barrier to Cryptosporidium and Giardia oocysts. Granular media filters require regular backwashing to remove retained particulate matter and retain effective performance. Many filter operational issues arise from poor backwashing. Within the industry, it is accepted that issues associated with ineffective backwashing include: mudball formation, grain cementation, filter cracking, media loss and disturbance of support media. These issues in turn lead to excessive washwater use, poor filtration and increased opex. A key challenge for the works operator therefore is to know when to instigate a backwash. The onset of breakthrough (identified via filtered water turbidity), unacceptably high headloss, or a pre set time interval can all be used to identify the need for filter cleaning via backwashing. Greater insight into, and understanding of, filter performance can help inform the operation of existing filters and also optimise the design and operation of new ones, thus optimising future capital investment.
This project will develop a multiphase numerical model of the filtration and backwashing processes in order to assess and optimise filtration performance and design. We propose to apply the Discrete Element Modelling (DEM) coupled with the Lattice Boltzmann Method (LBM) technique to simulate particle behaviour within a granular media filter. The model will incorporate the accepted particulate retention mechanisms (straining, sedimentation, interception and electrostatic adhesion). The strength of the coupled DEM-LBM technique means that we will be able to model and assess the impact of different media properties (acid solubility, density, size distribution, porosity, shape, surface characteristics, settling velocity, min fluidisation velocity, strength and friability of media, multimedia filtration) on filtration performance. However, in addition to assessing filtration performance, we will develop simulations of air scour and backwashing processes to generate a fluidised bed (e.g. air scour and fluidised bed water wash, combined air and water wash, collapse-pulsed backwash), such that optimised backwashing processes can be identified, not just the frequency of backwash.

Interested applicants should contact the project supervisor, Dr John Bridgeman, for an informal discussion.

To find out more about studying for a PhD at the University of Birmingham, including full details of the research undertaken in each school, the funding opportunities for each subject, and guidance on making your application, you can now order your copy of the new Doctoral Research Prospectus, at: www.birmingham.ac.uk/students/drp.aspx

Funding Notes

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more.

Non-UK Students: If you have the correct qualifications and access to your own funding, either from your home country or your own finances, your application to work on this project will be considered.

How good is research at University of Birmingham in Civil and Construction Engineering?

FTE Category A staff submitted: 18.10

Research output data provided by the Research Excellence Framework (REF)

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