4.8 Article

Predicting scale formation during electrodialytic nutrient recovery

Journal

WATER RESEARCH
Volume 110, Issue -, Pages 202-210

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2016.11.063

Keywords

Electrodialysis; Nutrient recovery; Membrane scaling; Modelling; Physico-chemistry; Electro-chemistry

Funding

  1. Grain Research & Development Corporation (GRDC) under the project Fertilizer from Waste Phase II [UQ00061]
  2. Grain Research Scholarship supporting Emma Thompson Brewster [GRS10661]

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Electro-concentration of nutrients from waste streams is a promising technology to enable resource recovery, but has several operational concerns. One key concern is the formation of inorganic scale on the concentrate side of cation exchange membranes when recovering nutrients from wastewaters containing calcium, magnesium, phosphorous and carbonate, commonly present in anaerobic digester rejection water. Electrodialytic nutrient recovery was trialed on anaerobic digester rejection water in a laboratory scale electro-concentration unit without treatment (A), following struvite recovery (B), and following struvite recovery as well as concentrate controlled at pH 5 for scaling control (C). Treatment A resulted in large amount of scale, while treatment B significantly reduced the amount of scale formation with reduction in magnesium phosphates, and treatment C reduced the amount of scale further by limiting the formation of calcium carbonates. Treatment C resulted in an 87 +/- 7% by weight reduction in scale compared to treatment A. A mechanistic model for the inorganic processes was validated using a previously published general precipitation model based on saturation index. The model attributed the reduction in struvite scale to the removal of phosphate during the struvite pre-treatment, and the reduction in calcium carbonate scale to pH control resulting in the stripping of carbonate as carbon dioxide gas. This indicates that multiple strategies may be required to control precipitation, and that mechanistic models can assist in developing a combined approach. (C) 2016 Elsevier Ltd. All rights reserved.

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