Protection of Water Distribution Networks (WDNs) against contamination events has a paramount importance. Either deliberate or accidental contamination of these infrastructures has strong negative consequences from both social and economical point of view. The project SMaRT-OnlineWDN aimed to develop methods and software solutions 1) to detect contamination from non-specific sensors, 2) to maintain an online water quantity and water quality model that is reliable and 3) to use the past model predictions to backtrack the potential sources of contaminations. For source identification, is more reliable velocities from an historical data base a substantial advantage compared to offline velocity predictions? The aim of this paper is to answer to this question and to report the main findings in the SMaRT-OnlineWDN project for contaminant source identification in an online context. The problem of source identification consists in determining the location and duration of a contamination taking into account sensor responses. Our solution is a two-step enumeration/exploration method. Firstly, we solve the transport equation in reverse time for enumeration of the potential solutions. This is made independent of the reaction kinetics of particular substances. The known boundary conditions are the responses of sensors that count the successive contaminant fronts arriving at each sensor. In the second exploration step a probability calculation for ranking of the candidate solutions is proposed with two general stochastic methods (minimum relative entropy or least squares methods). An extensive use of simplification methods is carried on both temporally and spatially on the dynamic graph. A sensitivity analysis is made with regards to the demand uncertainty. Results on real networks in France and Germany are presented.
Ung, Hervé; Piller, Olivier A.; Jochen, Deuerlein; Gilbert, Denis; and Idel, Montalvo, "Lessons Learned In Solving The Contaminant Source Identification In An Online Context" (2014). CUNY Academic Works.