Numerically Efficient Water Quality Modeling and Security Applications

Chemical and biological contaminants can enter a drinking water distribution system through one of the many access points to the network and can spread quickly affecting a very large area. This is of great concern, and water utilities need to consider effective tools and mitigation strategies to improve water network security. This work presents two components that have been integrated into EPA’s Water Security Toolkit, an open-source software package that includes a set of tools to help water utilities protect the public against potential contamination events. The first component is a novel water quality modeling framework referred to as Merlion. The linear system describing contaminant spread through the network at the core of Merlion provides several advantages and potential uses that are aligned with current emerging water security applications. This computational framework is able to efficiently generate an explicit mathematical model that can be easily embedded into larger mathematical system. Merlion can also be used to efficiently simulate a large number of scenarios speeding up current water security tools by an order of magnitude. The last component is a pair of mixed-integer linear programming (MILP) formulations for efficient source inversion and optimal sampling. The contaminant source inversion problem involves determining the source of contamination given a small set of measurements. The source inversion formulation is able to handle discrete positive/negative measurements from manual grab samples taken at different sampling cycles. In addition, sensor/sample placement formulations are extended to determine the optimal locations for the next manual sampling cycle. This approach is enabled by a strategy that significantly reduces the size of the Merlion water quality model, giving rise to a much smaller MILP that is solvable in a real-time setting. The approach is demonstrated on a large-scale water network model with over 12,000 nodes while considering over 100 timesteps. The results show the approach is successful in finding the source of contamination remarkably quickly, requiring a small number of sampling cycles and a small number of sampling teams. These tools are being integrated and tested with a real-time response system