Terranna Haxton

Flushing under Source Uncertainties

Upon determination of a possible contamination threat in a water distribution network, a variety of response actions (e.g., public notification and operational changes) can be pursued in order to minimize public health and economic impacts and ultimately return the utility to normal operations. Flushing is a relatively common operational response option employed by utilities to address water quality concerns. Previously, an optimal hydraulic response tool was developed to help identify the best hydrant locations to flush. However, in order to apply this tool the contaminant injection location needs to be known. In previous research efforts, either the injection location was assumed to be known, or a sensor coverage map, which displays all contamination incidents potentially detected by a sensor, was employed to identify all possible injection locations. While the flushing locations selected for a known source location were effective in reducing impacts, the locations selected based on sensor coverage maps were not as effective. Therefore, in this study, a source location algorithm based on an event backtracking analysis is used to identify the most likely source locations. An example network model and multiple injection locations are used to evaluate the effectiveness of this approach. In addition, the reduction in impacts between the three different source identification approaches (i.e., known, sensor coverage map, backtracking) were compared. Overall, knowing the contaminant injection location greatly influences the effectiveness of the flushing response. For this study, the smaller amount of possible source locations, the greater the reduction in impacts. If only one source location is identified, the impact reduction could be as high as 98%. However, when 18 possible sources were identified from the sensor coverage map approach, only a reduction of 2% was achieved.

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...

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.

Efficient reduction of optimal disinfectant booster station placement formulations for security of large-scale water distribution networks

ABSTRACT In response to a contamination incident in water distribution networks, disinfectant booster stations can be used to neutralize the contaminant and protect the public. In this paper, two mixed-integer linear programming formulations are proposed for optimal placement of disinfectant booster stations. The first formulation minimizes the contaminant mass consumed by the public, while the second formulation minimizes the number of people who ingest the contaminant above a mass threshold. The proposed formulations consider uncertainty in both the location and time of the contamination incident, resulting in an intractably large stochastic programming problem. This manuscript proposes a series of reductions that decrease the size of the problem by up to five orders of magnitude. The results show that the use of a small number of disinfectant booster stations can be very effective as a response strategy.

Evaluating Response Planning Initiatives: Modeling Assumptions

The potential for intentional contamination of the nation’s drinking water infrastructure has heightened utility awareness regarding distribution system security. Corrective actions implemented by a water utility following a contamination incident have the potential to significantly mitigate public health and infrastructure impacts. Many mitigation and response options are available (e.g., flushing at hydrants to remove contaminants from pipes, injecting disinfectant or decontamination agents at booster stations to treat the water or remove the contaminant from pipe walls, sampling at locations throughout the network to determine the extent of contamination, or instituting “Do Not Drink” or “Do Not Use” public advisories). For any given utility, some options might be more effective than others, and the effectiveness might depend on timing and other factors. Modeling and simulation studies can help utility decision-makers evaluate the effectiveness and feasibility of various response actions. However, utilities need to use realistic input parameters to ensure that modeling results are meaningful. This paper summarizes the input parameters needed to realistically model utility response options as well as lessons learned from discussions with two water utilities on the practicality of initiating specific response actions. The purpose of the utility discussions was to ground-truth modeling assumptions and eliminate impractical and inefficient response options, while also placing realistic bounds on input parameters. With more accurate information, the results from simulation and optimization models will be more acceptable to water utilities and policy makers. Generating plausible approaches to dealing with a contamination incident will support the utilities’ decision making process and facilitate selection of the most effective operational response. The value of this type of response planning is discussed for a wide audience of water utilities.