Decision-Making Frameworks For Using Sensor Data And Evolutionary Algorithms To Flush A Contaminated Water Distribution System

In the event that a contaminant enters a water distribution system, opening hydrants to flush contaminated water can protect consumers from becoming exposed. Strategies for operating hydrants can be developed to specify the selection of hydrants and the timing of operations to maintain a minimum water quality for every demand nodes in the network or maximize the amount of contaminant that is removed from the network. As an event unfolds, however, sensor data may be the only information that is available to indicate the location and timing of the contaminant source, and ultimately, hydrant strategies must be selected in a highly uncertain environment. The decision-making framework for making real-time decisions to select hydrant strategies relies on computational and sensor technologies, including the accuracy and precision of sensor data; the timeliness of data availability (e.g., streaming data or data that is collected manually); and computational capabilities to execute search simulation-optimization frameworks in real-time. This research will explore a decision-making framework to provide a library of response options that can be selected based on sensor data as an event unfolds. The library of hydrant strategies is developed a priori using a simulation-optimization framework. Potential sources are classified based on the order of sensors that are activated, and hydrant strategies are identified to maximize average performance for events within each class through the application of a genetic algorithm framework. The decision-making frameworks are applied and compared for a set of events that are simulated for two networks: the virtual city of Mesopolis and the town of Cary.