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A generalised chemical precipitation modelling approach in wastewater treatment applied to calcite

Overview of attention for article published in Water Research, January 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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1 policy source
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1 Facebook page

Citations

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98 Dimensions

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133 Mendeley
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Title
A generalised chemical precipitation modelling approach in wastewater treatment applied to calcite
Published in
Water Research, January 2015
DOI 10.1016/j.watres.2014.10.011
Pubmed ID
Authors

Christian Kazadi Mbamba, Damien J. Batstone, Xavier Flores-Alsina, Stephan Tait

Abstract

Process simulation models used across the wastewater industry have inherent limitations due to over-simplistic descriptions of important physico-chemical reactions, especially for mineral solids precipitation. As part of the efforts towards a larger Generalized Physicochemical Modelling Framework, the present study aims to identify a broadly applicable precipitation modelling approach. The study uses two experimental platforms applied to calcite precipitating from synthetic aqueous solutions to identify and validate the model approach. Firstly, dynamic pH titration tests are performed to define the baseline model approach. Constant Composition Method (CCM) experiments are then used to examine influence of environmental factors on the baseline approach. Results show that the baseline model should include precipitation kinetics (not be quasi-equilibrium), should include a 1st order effect of the mineral particulate state (Xcryst) and, for calcite, have a 2nd order dependency (exponent n = 2.05 ± 0.29) on thermodynamic supersaturation (σ). Parameter analysis indicated that the model was more tolerant to a fast kinetic coefficient (kcryst) and so, in general, it is recommended that a large kcryst value be nominally selected where insufficient process data is available. Zero seed (self nucleating) conditions were effectively represented by including arbitrarily small amounts of mineral phase in the initial conditions. Both of these aspects are important for wastewater modelling, where knowledge of kinetic coefficients is usually not available, and it is typically uncertain which precipitates are actually present. The CCM experiments confirmed the baseline model, particularly the dependency on supersaturation. Temperature was also identified as an influential factor that should be corrected for via an Arrhenius-style correction of kcryst. The influence of magnesium (a common and representative added impurity) on kcryst was found to be significant but was considered an optional correction because of a lesser influence as compared to that of temperature. Other variables such as ionic strength and pH were adequately captured by the quasi-equilibrium description of the aqueous-phase and no further kinetic corrections were required. The baseline model is readily expandable to include other precipitation reactions. For simple representations, large values for kcryst with n = 2 (or n = 2 or 3 for other minerals, as appropriate) should be selected without corrections to kcryst. Where accuracy is required (e.g., in mechanistic studies), machine estimation of kcryst should be performed with robust process data and kcryst should at least be corrected for temperature.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
France 1 <1%
Australia 1 <1%
Unknown 130 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 20%
Student > Master 25 19%
Researcher 19 14%
Student > Doctoral Student 9 7%
Student > Bachelor 8 6%
Other 15 11%
Unknown 30 23%
Readers by discipline Count As %
Engineering 35 26%
Chemistry 16 12%
Environmental Science 9 7%
Chemical Engineering 9 7%
Agricultural and Biological Sciences 5 4%
Other 16 12%
Unknown 43 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 June 2020.
All research outputs
#8,261,756
of 25,373,627 outputs
Outputs from Water Research
#3,025
of 11,875 outputs
Outputs of similar age
#105,034
of 359,528 outputs
Outputs of similar age from Water Research
#28
of 105 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 11,875 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 359,528 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.