4.8 Article

Predicting the settling velocity of flocs formed in water treatment using multiple fractal dimensions

Journal

WATER RESEARCH
Volume 46, Issue 13, Pages 4188-4194

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2012.04.031

Keywords

Fractal dimension; Settling velocity; Lime softening; Floc

Funding

  1. University of Manitoba
  2. NSERC
  3. Manitoba's Graduate Research Internship Program (MITACS)
  4. City of Portage la Prairie

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Here we introduce a distribution of floc fractal dimensions as opposed to a single fractal dimension value into the floc settling velocity model developed in earlier studies. The distribution of fractal dimensions for a single floc size was assumed to cover a range from 1.9 to 3.0. This range was selected based on the theoretically determined fractal dimensions for diffusion-limited and cluster cluster aggregation. These two aggregation mechanisms are involved in the formation of the lime softening flocs analyzed in this study. Fractal dimensions were generated under the assumption that a floc can have any value of normally distributed fractal dimensions ranging from 1.9-3.0. A range of settling velocities for a single floc size was calculated based on the distribution of fractal dimensions. The assumption of multiple fractal dimensions for a single floc size resulted in a non-unique relationship between the floc size and the floc settling velocity, i.e., several different settling velocities were calculated for one floc size. The settling velocities calculated according to the model ranged from 0 to 10 mm/s (average 2.22 mm/s) for the majority of flocs in the size range of 1-250 mu m (average 125 mu m). The experimentally measured settling velocities of flocs ranged from 0.1 to 7.1 mm/s (average 2.37 mm/s) for the flocs with equivalent diameters from 10 mu m to 260 mu m (average 124 mu m). Experimentally determined floc settling velocities were predicted well by the floc settling model incorporating distributions of floc fractal dimensions calculated based on the knowledge of the mechanisms of aggregation, i.e., cluster cluster aggregation and diffusion-limited aggregation. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

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