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A novel image processing-based system for turbidity measurement in domestic and industrial wastewater

Overview of attention for article published in Water Science & Technology, January 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#35 of 1,693)
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
8 tweeters

Citations

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10 Dimensions

Readers on

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36 Mendeley
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Title
A novel image processing-based system for turbidity measurement in domestic and industrial wastewater
Published in
Water Science & Technology, January 2018
DOI 10.2166/wst.2018.030
Pubmed ID
Authors

Darragh Mullins, Derek Coburn, Louise Hannon, Edward Jones, Eoghan Clifford, Martin Glavin

Abstract

Wastewater treatment facilities are continually challenged to meet both environmental regulations and reduce running costs (particularly energy and staffing costs). Improving the efficiency of operational monitoring at wastewater treatment plants (WWTPs) requires the development and implementation of appropriate performance metrics; particularly those that are easily measured, strongly correlate to WWTP performance, and can be easily automated, with a minimal amount of maintenance or intervention by human operators. Turbidity is the measure of the relative clarity of a fluid. It is an expression of the optical property that causes light to be scattered and absorbed by fine particles in suspension (rather than transmitted with no change in direction or flux level through a fluid sample). In wastewater treatment, turbidity is often used as an indicator of effluent quality, rather than an absolute performance metric, although correlations have been found between turbidity and suspended solids. Existing laboratory-based methods to measure turbidity for WWTPs, while relatively simple, require human intervention and are labour intensive. Automated systems for on-site measuring of wastewater effluent turbidity are not commonly used, while those present are largely based on submerged sensors that require regular cleaning and calibration due to fouling from particulate matter in fluids. This paper presents a novel, automated system for estimating fluid turbidity. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth, and computer vision processing techniques are used to quantify this characteristic. Results from the proposed system were compared with results from established laboratory-based methods and were found to be comparable. Tests were conducted using both synthetic dairy wastewater and effluent from multiple WWTPs, both municipal and industrial. This system has an advantage over current methods as it provides a multipoint analysis that can be easily repeated for large volumes of wastewater effluent. Although the system was specifically designed and tested for wastewater treatment applications, it could have applications such as in drinking water treatment, and in other areas where fluid turbidity is an important measurement.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 14%
Student > Master 4 11%
Student > Postgraduate 3 8%
Student > Doctoral Student 3 8%
Student > Ph. D. Student 3 8%
Other 9 25%
Unknown 9 25%
Readers by discipline Count As %
Engineering 11 31%
Environmental Science 6 17%
Chemical Engineering 2 6%
Medicine and Dentistry 2 6%
Physics and Astronomy 1 3%
Other 4 11%
Unknown 10 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 21 March 2018.
All research outputs
#1,974,519
of 12,689,756 outputs
Outputs from Water Science & Technology
#35
of 1,693 outputs
Outputs of similar age
#66,141
of 273,989 outputs
Outputs of similar age from Water Science & Technology
#3
of 25 outputs
Altmetric has tracked 12,689,756 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,693 research outputs from this source. They receive a mean Attention Score of 2.4. This one has done particularly well, scoring higher than 97% 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 273,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.