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Trajectory clustering approach for reducing water quality event false alarms

Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers

Vugrin, Eric D.; Mckenna, Sean A.; Hart, David B.

Event Detection Systems (EDS) performance is hindered by false alarms that cause unnecessary resource expenditure by the utility and undermine confidence in the EDS operation. Changes in water quality due to operational changes in the utility hydraulics can cause a significant number of false alarms. These changes may occur daily and each instance produces similar changes in the multivariate water quality pattern. Recognizing that patterns of water quality change must be identified, we adapt trajectory clustering as a means of classifying these multivariate patterns. We develop a general approach for dealing with changes in utility operations that impact water quality. This approach uses historical data water quality data from the utility to identify recurring patterns and retains those patterns in a library that can be accessed during online operation. We have implemented this pattern matching capability within CANARY and describe several example applications that demonstrate a decrease in false alarms. ©2009 ASCE.

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TYPE Conference YEAR 2009
ScopusOSTI

Quantitative resilience analysis through control design

Vugrin, Eric D.; Camphouse, Russell C.; Sunderland, Daniel S.

Critical infrastructure resilience has become a national priority for the U. S. Department of Homeland Security. System resilience has been studied for several decades in many different disciplines, but no standards or unifying methods exist for critical infrastructure resilience analysis. Few quantitative resilience methods exist, and those existing approaches tend to be rather simplistic and, hence, not capable of sufficiently assessing all aspects of critical infrastructure resilience. This report documents the results of a late-start Laboratory Directed Research and Development (LDRD) project that investigated the development of quantitative resilience through application of control design methods. Specifically, we conducted a survey of infrastructure models to assess what types of control design might be applicable for critical infrastructure resilience assessment. As a result of this survey, we developed a decision process that directs the resilience analyst to the control method that is most likely applicable to the system under consideration. Furthermore, we developed optimal control strategies for two sets of representative infrastructure systems to demonstrate how control methods could be used to assess the resilience of the systems to catastrophic disruptions. We present recommendations for future work to continue the development of quantitative resilience analysis methods.

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TYPE SAND Report YEAR 2009
OSTIDOI

Energy and water sector policy strategies for drought mitigation

Vugrin, Eric D.; Vargas, Vanessa N.

Tensions between the energy and water sectors occur when demand for electric power is high and water supply levels are low. There are several regions of the country, such as the western and southwestern states, where the confluence of energy and water is always strained due to population growth. However, for much of the country, this tension occurs at particular times of year (e.g., summer) or when a region is suffering from drought conditions. This report discusses prior work on the interdependencies between energy and water. It identifies the types of power plants that are most likely to be susceptible to water shortages, the regions of the country where this is most likely to occur, and policy options that can be applied in both the energy and water sectors to address the issue. The policy options are designed to be applied in the near term, applicable to all areas of the country, and to ease the tension between the energy and water sectors by addressing peak power demand or decreased water supply.

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TYPE SAND Report YEAR 2009
OSTIDOI

Markov models and the ensemble Kalman filter for estimation of sorption rates

Mckenna, Sean A.; Vugrin, Kay E.; Vugrin, Eric D.

Non-equilibrium sorption of contaminants in ground water systems is examined from the perspective of sorption rate estimation. A previously developed Markov transition probability model for solute transport is used in conjunction with a new conditional probability-based model of the sorption and desorption rates based on breakthrough curve data. Two models for prediction of spatially varying sorption and desorption rates along a one-dimensional streamline are developed. These models are a Markov model that utilizes conditional probabilities to determine the rates and an ensemble Kalman filter (EnKF) applied to the conditional probability method. Both approaches rely on a previously developed Markov-model of mass transfer, and both models assimilate the observed concentration data into the rate estimation at each observation time. Initial values of the rates are perturbed from the true values to form ensembles of rates and the ability of both estimation approaches to recover the true rates is examined over three different sets of perturbations. The models accurately estimate the rates when the mean of the perturbations are zero, the unbiased case. For the cases containing some bias, addition of the ensemble Kalman filter is shown to improve accuracy of the rate estimation by as much as an order of magnitude.

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TYPE SAND Report YEAR 2007
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Results 101–125 of 133
Results 101–125 of 133