Time Dependent Contamination Source Determination: A Network Subdomain Approach for Very Large Water Networks

Abstract

This paper presents an approach for identifying contamination sources in very large municipal water networks. The vulnerability of municipal drinking water networks to intentional and accidental contaminations requires investigation of alternative protection measures. If a contamination occurs, it is important to identify both the time and location of the contamination source. A dynamic optimization approach for estimating contamination sources was previously presented. The approach developed an origin tracking algorithm that reformulated the partial dieren tial pipe expressions, removing the need to discretize in space. Although this allowed for ecien t solutions using a direct simultaneous technique for a network of approximately 500 nodes, the approach does not scale indenitely to very large networks. This current paper handles very large water networks by performing the optimization on a smaller, subdomain of the entire network. This approach considers the hydraulics and sensor measurements for the entire network, but formulates the dynamic optimization problem for a subset of the network nodes. A subdomain approach is introduced, forming a geographic window around the rst sensor to detect contaminant. Numerical results indicate that this subdomain approach is eectiv e at identifying contamination sources. Furthermore, since the required subdomain size is not dependent on the size of the entire network, this approach scales to very large municipal water networks.