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CLC number: TU991.33

On-line Access: 2012-07-03

Received: 2011-10-24

Revision Accepted: 2012-03-27

Crosschecked: 2012-05-29

Cited: 4

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2012 Vol.13 No.7 P.559-570

10.1631/jzus.A1100286


Identification of sources of pollution and contamination in water distribution networks based on pattern recognition


Author(s):  Tao Tao, Ying-jun Lu, Xiang Fu, Kun-lun Xin

Affiliation(s):  College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   taotao@tongji.edu.cn

Key Words:  Contamination, Identification, Water distribution network (WDN)


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Tao Tao, Ying-jun Lu, Xiang Fu, Kun-lun Xin. Identification of sources of pollution and contamination in water distribution networks based on pattern recognition[J]. Journal of Zhejiang University Science A, 2012, 13(7): 559-570.

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Abstract: 
An intrusion of contaminants into the water distribution network (WDN) can occur through storage tanks (via animals, dust-carrying bacteria, and infiltration) and pipes. A sensor network could yield useful observations that help identify the location of the source, the strength, the time of occurrence, and the duration of contamination. This paper proposes a methodology for identifying the contamination sources in a water distribution system, which identifies the key characteristics of contamination, such as location, starting time, and injection rates at different time intervals. Based on simplified hypotheses and associated with a high computational efficiency, the methodology is designed to be a simple and easy-to-use tool for water companies to ensure rapid identification of the contamination sources, The proposed methodology identifies the characteristics of pollution sources by matching the dynamic patterns of the simulated and measured concentrations. The application of this methodology to a literature network and a real WDN are illustrated with the aid of an example. The results showed that if contaminants are transported from the sources to the sensors at intervals, then this method can identify the most possible ones from candidate pollution sources. However, if the contamination data is minimal, a greater number of redundant contamination source nodes will be present. Consequently, more data from different sensors obtained through network monitoring are required to effectively use this method for locating multi-sources of contamination in the WDN.

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