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Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

Overview of attention for article published in Water Research, April 2003
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

policy
1 policy source

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
59 Mendeley
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Title
Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis
Published in
Water Research, April 2003
DOI 10.1016/s0043-1354(02)00494-3
Pubmed ID
Authors

Yoon-Seok Timothy Hong, Michael R. Rosen, Rao Bhamidimarri

Abstract

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Brazil 2 3%
Italy 1 2%
Germany 1 2%
Japan 1 2%
Spain 1 2%
Unknown 50 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 32%
Researcher 10 17%
Student > Master 8 14%
Student > Doctoral Student 6 10%
Professor > Associate Professor 5 8%
Other 11 19%
Readers by discipline Count As %
Engineering 23 39%
Unspecified 13 22%
Environmental Science 11 19%
Agricultural and Biological Sciences 4 7%
Earth and Planetary Sciences 3 5%
Other 5 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 01 January 2009.
All research outputs
#3,517,643
of 12,269,726 outputs
Outputs from Water Research
#1,215
of 5,590 outputs
Outputs of similar age
#114,321
of 338,768 outputs
Outputs of similar age from Water Research
#33
of 178 outputs
Altmetric has tracked 12,269,726 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,590 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 338,768 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 63% of its contemporaries.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.