Consequence Management Utilizing Optimization

Abstract

Following the identification of a contaminant in a water distribution network, a variety of response actions must be examined in order to implement the most beneficial consequence management strategy. Optimization techniques can be employed to determine the cost/benefit of reducing impacts to the network from contamination by isolating and/or flushing the system. In this current effort, we employ a genetic algorithm to minimize contaminant concentrations in a network while minimizing the cost of demand alteration. Application of this technique to two relatively simple networks demonstrates the usefulness of this optimization method as a consequence management strategy to reduce contaminant concentration. For the EPANET Example 1 network, the optimal response solution included closure of two pipes and alteration of the demand at one node, reducing the total network concentration by 95%, with a 73% increase in total network demand. For the Anytown network, the optimal response solution included altering the demand at four nodes, which resulted in a 12% increase in total network demand, while closing four pipes reduced the total network concentration by 54%.