SMaRT-OnlineWDN: A Franco-German Project For The Online Security Management Of Water Distribution Networks

Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination which need to be detected in due time. However, until now, no monitoring system is capable of protecting a WDN in real time. Powerful online sensor systems are currently developed and the prototypes are able to detect a small change in water quality. In the immediate future, water service utilities will install their networks with water quantity and water quality sensors. For taking appropriate decisions and countermeasures, WDN operators will need to dispose of: 1) a fast and reliable detection of abnormal events in the WDNs; 2) reliable online models both for the hydraulics and water quality predictions; 3) methods for contaminant source identification backtracking from the data history. Actually, in general none of these issues (1) – (3) are available at the water suppliers. Consequently, the main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for WDNs that is based on sensor measurements of water quality as well as water quantity. Its main innovations are the detection of abnormal events with a binary classifier of high accuracy and the generation of real-time, reliable (i) flow and pressure predictions, (ii) water quality indicator predictions of the whole water network. Detailed information regarding contamination sources (localization and intensity) will be explored by means of the online running model, which is automatically calibrated to the measured sensor data. Its field of application ranges from the detection of deliberate contamination including source identification and decision support for effective countermeasures to improved operation and control of a WDN under normal and abnormal conditions (dual benefit).In this project, the technical research work is completed with a sociological, economical and management analysis

Lessons Learned In Solving The Contaminant Source Identification In An Online Context

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

Oligocene and early Miocene mammal biostratigraphy of the Valley of Lakes in Mongolia

The Taatsiin Gol Basin in Mongolia is a key area for understanding the evolution and dispersal of Central Asian mammal faunas during the Oligocene and early Miocene. After two decades of intense fieldwork, the area is extraordinarily well sampled and taxonomically well studied, yielding a large dataset of 19,042 specimens from 60 samples. The specimens represent 176 species-level and 99 genus-level taxa comprising 135 small mammal species and 47 large mammals. A detailed lithostratigraphy and new magnetostratigraphic and radiometric datings provide an excellent frame for these biotic data. Therefore, we test and evaluate the informal biozonation scheme that has been traditionally used for biostratigraphic correlations within the basin. Based on the analysis of the huge dataset, a formalised biostratigraphic scheme is proposed. It comprises the Cricetops dormitor Taxon Range Zone (Rupelian), subdivided into the Allosminthus khandae Taxon Range Subzone and the Huangomys frequens Abundance Subzone, the Amphechinus taatsiingolensis Abundance Zone (early Chattian), the Amphechinus major Taxon Range Zone (late Chattian), subdivided into the Yindirtemys deflexus Abundance Subzone and the Upper Amphechinus major T. R. Z., and the Tachyoryctoides kokonorensis Taxon Range Zone (Aquitanian). In statistical analyses, samples attributed to these biozones form distinct clusters, indicating that each biozone was also characterised by a distinct faunal type

Adaptive Modeling Of Water Supply Networks For Improved Practical Applicability Of Hydraulic Online-Simulation

Online-Simulation of water distribution networks allows for estimating the current state of the entire network in near real-time. Measurement data coming from sensors at selected positions in the real network are used for driving a mathematical simulation model. Therefore the information gained from the measurements is extended and covers the whole system. As part of online monitoring or decision support systems online-simulations have multiple applications in operations and control of water supply networks. Although sensors and techniques for data transfer as well as mathematical simulation techniques are highly developed the practical applicability of online-simulations for decision support in large networks still suffers from the high time requirements for the whole cycle of measurement data updates, simulation and post-processing. One common approach to tackle this problem is the aggregation of the underlying models. However, for some applications like contaminant source identification the information that is lost can be crucial for the reliability of the decisions that are made based on the online-simulation results. In order to enhance online calculations and to improve the practical applicability of large online simulation models an adaptive calculation framework has been developed that allows for running the model with different levels of accuracy but using all one and the same data base. For each problem an adequate level of accuracy is chosen. Higher functions like source identification or optimisation algorithms that require a number of extra simulations can be focused on selected regions of the network. For the subnetwork in question detailed data are used whereas the rest of the system is omitted or, if necessary, considered with a lower level of accuracy. The framework is based on topological analysis and decomposition of the network graph. The paper describes the basic concepts and demonstrates its applicability by means of contaminant source identification

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany

Meta-heuristic versus gradient-type methods for real time demand calibration in water distribution system

Water distribution system modeling in real time involves many challenges for water utilities. Calibration of models to match measurements coming from a water network with calculation resulting from models is a necessary step to make models reliable. There are many variables involved in the analysis of a water distribution system and each of them contains a determined level of uncertainties. Nevertheless, in a real time scenario it is significant the impact that water demands at nodes could have compared with other variables. Demands, in contrast to pipe roughness for example, are much more sensitive to changes in time. A better estimation of how much water is being consumed at each point can help a lot to assess properly the current state of the network. This information is crucial for supporting decision making processes based on models working online and can be used as a starting point for decisions involving a short term forecasting of the network behavior. Among the challenges of the presented research it should be mentioned the short available time for providing calibration results in a “real time” context. The methods used in this paper are not only trying to achieve good results but to achieve results in a very short period of time. This idea has been accomplished by combining different heuristics, data mining and optimization techniques. Results presented in an online context are calculated based in the analysis of historical data and the analysis of the current information coming from water networks. For testing purposes, this research also includes the development of a software component for emulating the data transmission from the water network to an OPC server and from there to the algorithms in charge of demand estimation. A conclusion is also given about merit of the Meta-heuristic versus Gradient-type calibration methods

The French Cochlear Implant Registry (EPIIC): Perception and language results in infants with cochlear implantation under the age of 24 months

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