Multi-aspect Performance Analysis of Water Distribution Systems Under Pipe Failure

AbstractPerformance analysis of a water distribution system (WDS) under pipe failure can be studied from different aspects. Performance analysis techniques are mostly restricted to the single performance index of supply ratio. This research applied novel technique of octant analysis to study the performance of a WDS in three aspects of adequacy, equity and efficiency of water delivery simultaneously. Results reveal that evaluating the performance of a WDS in just one aspect cannot guaranty the perfect overall performance of the WDS in future from all aspects. This study recommends octant/quadrant analysis as a more comprehensive tool for multi-aspect performance analysis of WDSs

Flexibility Ranking of Water Distribution System Designs Under Future Mechanical and Hydraulic Uncertainty

AbstractAnnually a large amount of money should be spent by water authorities to adapt and update water distribution systems (WDSs)to the latest client's needs and variations known as adaptation cost. To prevent or lessen WDSs’ adaptation cost it is essential to insert a level of flexibility into WDS layouts from the very beginning in planning or designing stages [1]. This study proposed a simple technique based on multi-criteria decision analysis to rank a set of WDS layouts based on their level of flexibility under future mechanical and hydraulic uncertainty

Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor

Informational entropy : a failure tolerance and reliability surrogate for water distribution networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies