Katherine A. Klise

Water quality change detection: multivariate algorithms

In light of growing concern over the safety and security of our nations drinking water, increased attention has been focused on advanced monitoring of water distribution systems. The key to these advanced monitoring systems lies in the combination of real time data and robust statistical analysis. Currently available data streams from sensors provide near real time information on water quality. Combining these data streams with change detection algorithms, this project aims to develop automated monitoring techniques that will classify real time data and denote anomalous water types. Here, water quality data in 1 hour increments over 3000 hours at 4 locations are used to test multivariate algorithms to detect anomalous water quality events. The algorithms use all available water quality sensors to measure deviation from expected water quality. Simulated anomalous water quality events are added to the measured data to test three approaches to measure this deviation. These approaches include multivariate distance measures to 1) the previous observation, 2) the closest observation in multivariate space, and 3) the closest cluster of previous water quality observations. Clusters are established using kmeans classification. Each approach uses a moving window of previous water quality measurements to classify the current measurement as normal or anomalous. Receiver Operating Characteristic (ROC) curves test the ability of each approach to discriminate between normal and anomalous water quality using a variety of thresholds and simulated anomalous events. These analyses result in a better understanding of the deviation from normal water quality that is necessary to sound an alarm.

Multivariate Applications for Detecting Anomalous Water Quality

The ability to detect deliberate or accidental contamination of a water distribution system is of real concern to the safety and security of our nation’s drinking water. To address these concerns, increased attention has been placed on sophisticated monitoring of water distribution systems and the use of robust statistical analysis. Using existing data from in-situ water quality sensors, this paper explores the ability to detect anomalies in water quality using multivariate techniques. The algorithm developed in this study uses a multivariate distance measure between the current water quality measurement and the closest observation in multivariate space within a moving window of previous observations. To discriminate between normal and anomalous water quality, the distance measure is compared to a constant threshold. To test the algorithm, we utilize both simulated anomalous events and laboratory based events that correspond to real contaminants. These events are superimposed onto in-situ water quality recorded at four different locations within a single utility network. Measured water quality parameters include free chlorine, pH, temperature and electrical conductivity. Robust discrimination methods have a high probability of detecting anomalies with a low false alarm rate. Here, receiver operating characteristic (ROC) curves are used to test the ability of the multivariate classification algorithm to detect anomalous water quality while keeping false alarms low. This analysis explores the false alarm rate associated with detecting a range of anomalous water quality observations.

TESTING WATER QUALITY CHANGE DETECTION ALGORITHMS.

Rapid detection of anomalous operating conditions within a water distribution network is desirable for the protection of the network against both accidental and malevolent contamination events. In the absence of a suite of in-situ, real-time sensors that can accurately identify a wide range of contaminants, we focus on detecting changes in water quality through analysis of existing data streams from in-situ water quality sensors. Three different change detection algorithms are tested: time series increments, linear filter and multivariate distance. Each of these three algorithms uses previous observations of the water quality to predict future water quality values. Large deviations between the predicted or previously measured values and observed values at future times indicate a change in the expected water quality. The definition of what constitutes a large deviation is quantified by a threshold value applied to the observed differences. Both simulated time series of water quality as well as measured chlorine residual values from two different locations within a distribution network are used as the background water quality values. The simulated time series are created specifically to challenge the change detection algorithms with bimodally distributed water quality values having a square wave and sin wave time series, with and without correlated noise. Additionally, a simulated time series resembling observed water quality time series is created with different levels of variability. The algorithms are tested in two different ways. First, background water quality without any anomalous events are used to test the ability of each algorithm to identify the water quality value at the next time step. Summary statistics on the prediction errors as well as the number of false positive detections quantify the ability of each algorithm to predict the background water quality. The performance of the algorithms with respect to limiting false positives is also compared against a simpler “set point” approach to detecting water quality changes. The second mode of testing employs events in the form of square waves superimposed on top of modeled/measured background water quality data. Three different event strengths are examined and the event detection capabilities of each algorithm are evaluated through the use of receiver operating characteristic (ROC) curves. The area under the ROC curve provides a quantitative basis of comparison across the three algorithms. Results show that the multivariate algorithm produces the lowest prediction errors for all cases of background water quality. A comparison of the number of false positives reported from the change detection algorithms and a set point approach highlights the efficiency of the change detection algorithms. Across all three algorithms, most prediction errors are within one standard deviation of the mean water quality. The event detection results show that the best performing algorithm varies across different background water quality models and simulated event strength.

Testing Contamination Source Identification Methods for Water Distribution Networks

AbstractIn the event of contamination in a water distribution network (WDN), source identification (SI) methods that analyze sensor data can be used to identify the source location(s). Knowledge of the source location and characteristics are important to inform contamination control and cleanup operations. Various SI strategies that have been developed by researchers differ in their underlying assumptions and solution techniques. The following manuscript presents a systematic procedure for testing and evaluating SI methods. The performance of these SI methods is affected by various factors including the size of WDN model, measurement error, modeling error, time and number of contaminant injections, and time and number of measurements. This paper includes test cases that vary these factors and evaluates three SI methods on the basis of accuracy and specificity. The tests are used to review and compare these different SI methods, highlighting their strengths in handling various identification scenarios. The...

Dependence on geographic location of air mass modifiers for photovoltaic module performance models

Air mass modifiers are frequently used to represent the effects of solar spectrum on PV module current. Existing PV module performance models assume a single empirical expression, a polynomial in air mass, for all locations and times. In this paper, air mass modifiers are estimated for several modules of different types from IV curves measured with the modules at fixed orientation in three climatically different locations around the United States. Systematic variation is found in the effect of solar spectrum on PV module current that is not well approximated by the standard air mass modifier polynomial.

Applications of fractured continuum model to enhanced geothermal system heat extraction problems

This paper describes the applications of the fractured continuum model to the different enhanced geothermal systems reservoir conditions. The capability of the fractured continuum model to generate fracture characteristics expected in enhanced geothermal systems reservoir environments are demonstrated for single and multiple sets of fractures. Fracture characteristics are defined by fracture strike, dip, spacing, and aperture. The paper demonstrates how the fractured continuum model can be extended to represent continuous fractured features, such as long fractures, and the conditions in which the fracture density varies within the different depth intervals. Simulations of heat transport using different fracture settings were compared with regard to their heat extraction effectiveness. The best heat extraction was obtained in the case when fractures were horizontal. A conventional heat extraction scheme with vertical wells was compared to an alternative scheme with horizontal wells. The heat extraction with the horizontal wells was significantly better than with the vertical wells when the injector was at the bottom.

Computational thermal, chemical, fluid, and solid mechanics for geosystems management.

This document summarizes research performed under the SNL LDRD entitled - Computational Mechanics for Geosystems Management to Support the Energy and Natural Resources Mission. The main accomplishment was development of a foundational SNL capability for computational thermal, chemical, fluid, and solid mechanics analysis of geosystems. The code was developed within the SNL Sierra software system. This report summarizes the capabilities of the simulation code and the supporting research and development conducted under this LDRD. The main goal of this project was the development of a foundational capability for coupled thermal, hydrological, mechanical, chemical (THMC) simulation of heterogeneous geosystems utilizing massively parallel processing. To solve these complex issues, this project integrated research in numerical mathematics and algorithms for chemically reactive multiphase systems with computer science research in adaptive coupled solution control and framework architecture. This report summarizes and demonstrates the capabilities that were developed together with the supporting research underlying the models. Key accomplishments are: (1) General capability for modeling nonisothermal, multiphase, multicomponent flow in heterogeneous porous geologic materials; (2) General capability to model multiphase reactive transport of species in heterogeneous porous media; (3) Constitutive models for describing real, general geomaterials under multiphase conditions utilizing laboratory data; (4) General capability to couple nonisothermal reactive flow with geomechanics (THMC); (5) Phase behavior thermodynamics for the CO2-H2O-NaCl system. General implementation enables modeling of other fluid mixtures. Adaptive look-up tables enable thermodynamic capability to other simulators; (6) Capability for statistical modeling of heterogeneity in geologic materials; and (7) Simulator utilizes unstructured grids on parallel processing computers.

A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Python™ package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.

Calibration of Photovoltaic Module Performance Models Using Monitored System Data

Calibration of a photovoltaic module performance model currently relies on measurements of electrical output taken with the module outdoors on a two-axis tracker, or indoors using a solar simulator. These measurements require expensive infrastructure. By contrast, measuring electrical performance for systems outdoors on fixed racking is substantially cheaper, yet no method currently exists to translate these meaurements to model coefficients. We present and validate methods to calibrate the Sandia Photovoltaic Array Performance Model and the California Energy Commission model using data collected outdoors for modules at fixed tilt orientation. A method to successfully calibrate module performance models without recourse to a two-axis tracker or a solar simulator expands the ability to rapidly characterize photovoltaic modules in actual operating conditions.

Formulation of Chlorine and Decontamination Booster Station Optimization Problem.

A commonly used indicator of water quality is the amount of residual chlorine in a water distribution system. Chlorine booster stations are often utilized to maintain acceptable levels of residual chlorine throughout the network. In addition, hyper-chlorination has been used to disinfect portions of the distribution system following a pipe break. Consequently, it is natural to use hyper-chlorination via multiple booster stations located throughout a network to mitigate consequences and decontaminate networks after a contamination event. Many researchers have explored different methodologies for optimally locating booster stations in the network for daily operations. In this research, the problem of optimally locating chlorine booster stations to decontaminate following a contamination incident will be described.