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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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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

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Brazil 2 2%
Germany 1 1%
Italy 1 1%
Japan 1 1%
Spain 1 1%
Unknown 85 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 24%
Researcher 14 15%
Student > Master 13 14%
Student > Doctoral Student 7 7%
Professor 4 4%
Other 15 16%
Unknown 18 19%
Readers by discipline Count As %
Engineering 37 39%
Environmental Science 13 14%
Earth and Planetary Sciences 3 3%
Chemical Engineering 3 3%
Agricultural and Biological Sciences 3 3%
Other 7 7%
Unknown 28 30%