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RSSMulti Objective Optimization Of Sensor Placement In Water Distribution Systems
October 12, 2006
Click Here To Download:•White Paper: Multi Objective Optimization Of Sensor Placement In Water Distribution Systems
Placement of water quality sensor has received an increasing concern for timely providing the warning of possible contamination in a water system. Due to the large dimension of water distribution network and the difficulty for predicting where a contamination event occurs, it is a great challenge for engineers to come up with good sensor locations with any confidence to effectively detect possible contamination events. The problem is complicated by the fact that sensor location is evaluated against a number of objective criteria that may include the detection likelihood, the expected detection time, affected population and contaminated water consumption. A design that improves one objective may deteriorate another. In this paper, sensor placement is formulated as a multi objective optimization problem that is solved by using a competent genetic algorithm while the contamination events are simulated by the latest development of Monte Carlo method.
Placing reliable and effective water sensors is of great importance for provision of secure water service and early warning system for detecting a contamination event. However, finding the best location for water quality sensors is a challenging problem because of the large number of possible contamination events and sensor locations. For a typical system, there is no guarantee that a global optimum solution can be achieved. However, using optimization, it should be possible to find better locations than those that would be selected randomly.
Over last decade, a number of methods have been developed and applied to optimizing water sensor placement. Lee and Deininger (1992) developed an integer programming optimization model to maximize the sensor coverage with minimum number of sensors. The model is based upon steady state simulation and network connectivity. The same model was solved by Kumar et al. (1997) using a heuristic-based optimization method and by Al-Zahrami and Moised (2001) using genetic algorithm.
Berry et al. (2005) proposed a sensor placement model that minimizes the contamination risk using sensors that are placed on edges or pipes between two nodes. The risk for a node is evaluated as the multiplication of population density, contamination indicator (1 for contaminated or 0 for noncontaminated node) and the probability of an attack. The overall risk is the sum of individual node risk over a number of flow patterns over an extended period of time. Thus water quality model is incorporated into the model to simulate the response of a certain attack to come up with a correct contamination indicator for a node. The problem is solved by using integer programming. Shashtri and Diwekar (2006) extended Berry's work by considering demand uncertainty.
In the meantime, Ostfeld and Salomons (2005) uncovered a number of shortcomings of Lee and Deininger's method based upon network connectivity and steady state simulation. The pollution matrix approach originally proposed by Kessler et al (1998) was extended by incorporating an EPS water quality simulation and the optimization sensor placement was solved by using a genetic algorithm (Ostfeld and Salmons 2005). This method is to construct a matrix relation to contain information of whether or not a node is contaminated by a contamination event, which can be deliberate injection at a random node during a random time period. Each node is perceived as a possible sensor location. The more contamination events are observed or recorded at one node, the greater the detection likelihood is counted, the better sensor placement the node can be. A combination of certain number of nodes represents a sensor placement solution. A genetic algorithm is applied to optimize the sensor placement under a single optimization objective of maximizing detection likelihood.
However, the sensor placement is governed by multiple criteria such as detection time, contaminated water volume and affected population. In this paper, the pollution matrix is generalized to contain more information for evaluating multi criteria for sensor allocations. The optimization problem is solved by using the competent genetic algorithm (Wu and Simpson 2001). Two benchmark design network are demonstrated for the application of the method.
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Click Here To Download:•White Paper: Multi Objective Optimization Of Sensor Placement In Water Distribution Systems
SOURCE: Bentley Systems, Inc.
