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A mechanistic model for electrochemical nutrient recovery systems

Overview of attention for article published in Water Research, February 2016
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Title
A mechanistic model for electrochemical nutrient recovery systems
Published in
Water Research, February 2016
DOI 10.1016/j.watres.2016.02.032
Pubmed ID
Authors

Emma Thompson Brewster, Chirag M. Mehta, Jelena Radjenovic, Damien J. Batstone

Abstract

Electrochemical membrane technologies such as electrodialysis have been identified as key technologies to enable nutrient recovery from wastewater. However, current electrochemical models are focused on simpler solutions than wastewater and omit key outputs such as pH, or total cell potential. A combined physico-chemical and electrochemical model was developed which includes the mechanisms of competitive transport of ions, implicit inclusion of H(+) and OH(-), pH (including ionic activity and ion pairing), different factors contributing to total cell potential and a novel method for ion exchange membrane transport. The model outputs compare well with measurements from experiments and simulate secondary effects such as electrode reactions and current leakage. Results found that membrane, rather than boundary layer or bulk resistance was the major contributor to potential drop, and that apparent boundary layers were relatively thick (3 ± 1 mm). Non-ideal solution effects such as ion-pairing and ionic activity had a major impact, particularly on multi-valent Ca(2+) ions, which enhances the capability of electrodialysis to recover monovalent nutrient ions such as K(+) and NH4(+). Decreased resistivity of ion exchange membranes to specific ions (for example, in this case nitrate) could also be detected. The methods here are validated using a comparatively simple synthetic solution of five ionic components, but are able to be easily scaled for a more complex solution, and are also compatible with additional mechanisms such as precipitation, fouling, and scaling.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 32%
Student > Master 15 19%
Researcher 9 11%
Student > Doctoral Student 4 5%
Student > Bachelor 4 5%
Other 9 11%
Unknown 13 16%
Readers by discipline Count As %
Environmental Science 21 27%
Engineering 17 22%
Chemical Engineering 6 8%
Agricultural and Biological Sciences 2 3%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 9 11%
Unknown 22 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 March 2016.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Water Research
#9,085
of 11,875 outputs
Outputs of similar age
#268,485
of 311,617 outputs
Outputs of similar age from Water Research
#118
of 186 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,875 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 186 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.