Using In-Pipe Data Sonde Arrays for Security and Quality Monitoring in the Water Distribution System

Author(s):  
Dan Kroll ◽  
Karl King ◽  
Greg Klein
2020 ◽  
Vol 71 (1) ◽  
pp. 327-334
Author(s):  
Albert Titus Constantin ◽  
Gheorghe I. Lazar ◽  
Serban-Vlad Nicoara

The discrete numerical model developed by the help of TEVA-SPOT specialized software toolkit serves to a subsequent analysis that looks to estimate the water distribution system vulnerability in case of a contaminant agent release. The optimum location of the water quality sensors attached to a number of joints in the Timisoara (Romania) metropolitan water supply network can be reached in order to warn the company management and competent authorities and so to reduce the contamination effects upon the consumers.


2018 ◽  
Vol 20 (6) ◽  
pp. 1323-1342 ◽  
Author(s):  
Nathan Sankary ◽  
Avi Ostfeld

Abstract Placing fixed water quality monitoring stations in a water distribution system can greatly improve the security of the system via prompt detection of poor water quality. In the event that a harmful substance is injected into a water distribution system, large populations can be put at risk of exposure to the contaminant. Promptly detecting the presence of a contaminant will reduce the number of people put at risk of exposure. However, to protect against a wide variety of possible contaminants, a water quality monitoring station will need to identify contamination via recognition of anomalous changes in a suite of surrogate water quality indicators (chlorine, pH, etc.). This work attempts to place water quality monitoring stations within the water distribution at locations that best detect contamination events via surrogate water quality signals. Networks of water quality monitoring stations are designed to minimize the population affected prior to contamination event detection, and simultaneously minimize the expected number of false positive detections, under uncertain water quality conditions. Solutions generated in this study are compared to solutions designed via classical detection methods. Results show the sensor networks designed without consideration to detection via surrogate water quality parameters have higher false positive detection rates.


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