(595g) Source Inversion and Response Management in Large-Scale Water Distribution Systems | AIChE

(595g) Source Inversion and Response Management in Large-Scale Water Distribution Systems


Laird, C. D. - Presenter, Texas A&M University
Wong, A. V. - Presenter, Texas A&M University
McKenna, S. - Presenter, Sandia National Laboratories
Hart, W. - Presenter, Sandia National Laboratories

Ingression of chemical and biological contaminants into water distribution systems can occur via accidental or intentional contamination. Several researchers have proposed the development of early-warning detection systems based on an installed sensor network within the distribution system. However, given the countless number of possible contaminants that could affect the drinking water distribution system, it is not possible to devise sensors able to detect each and every individual contaminant. Instead, systems like CANARY (Sandia National Laboratories) have been developed to detect the presence of contaminants. CANARY relies on a fault detection approach using standard water quality parameters (e.g. pH, salinity, etc) available via routine manual grab samples and/or continuous water quality monitoring sensors at various access points to the network or at main monitoring facilities.

However, detecting the presence of a contaminant is only a part of the overall contamination problem. Once an initial detection is made, utility personnel must take additional grab samples and determine the source of the contamination so it can be stopped. Then, a detailed cleanup and control strategy must be developed to return the water distribution system to an operational state. We have been working actively with researchers at Sandia National Laboratories and engineers at PUB Singapore to develop an integrated optimal response management package for protection of water distribution systems based on a series of mixed-integer programming formulations.

First, we present a large-scale mixed-integer linear program (MILP) formulation for performing source inversion in drinking water systems using discrete (yes/no) measurements available from existing grab samples. The water quality model is developed using an origin-tracking approach and then exactly and efficiently reduced prior to the formulation of the MILP, giving rise to a much smaller MILP that is solvable in a real-time setting. However, given sparse measurement information, it is unlikely that a unique contamination source location can be immediately identified, and subsequent sampling cycles are necessary. We have developed another MILP formulation that determines optimal sampling locations by maximizing the number of distinguishable pairs of contamination events, while considering limited resources (sampling teams). Once enough sampling cycles have been performed, and the contamination location has been identified, it is necessary to determine a control and cleanup strategy for the event. For this problem, we have developed mixed-integer nonlinear programming (MINLP) formulation that considers rigorous hydraulics of the distribution system and determines the optimal control profiles for cleanup.

These formulations have been integrated within a software framework for optimal response management of contamination events in a water distribution system. Working closely with PUB in Singapore, we present several case-studies illustrating the effectiveness of this framework on a real large-scale network from the literature which has over 10,000 nodes and whose simulations consider over 100 timesteps.

Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.