Monday, February 23, 2009

Distributions Systems II

Berry, J.W., Fleisher, L., Hart, W.E., Phillips, C.A., Watson, JP (2005) “Sensor Placement in Municipal Water Networks” J. Water Res. Plan. Mang. 131(3): pp 237- 243

In this paper Berry et al. expand on principles established in Lee and Deininger by applying integer programming to the problem of detecting contamination within a water distribution system. Although regulations requiring the monitoring of water quality within a distribution system have been around since the enactment of the Clean Water Act the events of September 11th brought the need for more rigorous monitoring of water distribution systems to the forefront.

In contrast to the previously reviewed work by Lee and Deininger, Berry et al were interested in detecting and minimizing the impacts of intentional contamination of a water supply. As such they were required to take into account the usage and population density at each demand node as a function of time. To further complicate the optimization the site where the contaminant was introduced to the water supply was allowed to vary between optimizations based on time of day and location. By using synthetic probability distributions Berry et al. were able to develop a compromise optimization that was designed to minimize the impacts of an intentional contamination.

Berry et al. followed a design process similar to the methods used by Lee and Deininger by employing increasingly complex hypothetical models before using a real world system. One key difference between the two papers is the location of the sensors within the system. While Lee and Deininger placed their sensors are distribution nodes Berry et al. placed the sensors in the pipes between the nodes. Although this might seem like a small difference it allows for contamination to be detected en route from one node to another and in theory would allow for a response to begin before the contaminant reached the next node. By assigning a weight to each node based on population density and use Berry et al. were able to create an optimized solution that placed sensors in areas where contamination would cause highest potential impacts.

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