World Environmental and Water Resources Congress 2013: Showcasing the Future © ASCE 2013
Water Quality Sensor Placement Guidance using TEVA-SPOT Stacey Schal¹, Amanda Lothes², L. Sebastian Bryson³, Lindell Ormsbee4 1
Research Assistant, Department of Civil Engineering, University of Kentucky, 161 Raymond Building, Lexington, KY 40506, email:
[email protected] 2 Research Assistant, Department of Civil Engineering, University of Kentucky, 161 Raymond Building, Lexington, KY 40506, email:
[email protected] 3 Associate Professor, Department of Civil Engineering, University of Kentucky, 161 Raymond Building, Lexington, KY 40506, ph: (859) 257-3247, email:
[email protected] 4
Director, Kentucky Water Resources Research Inst., University of Kentucky, 233 Mining & Minerals Resource Building, Lexington, KY 40506, ph: (859) 257-1299, email:
[email protected] Abstract Contamination warning systems involve a network of sensors that can assess the water quality in a water distribution system and alert an operator of a potential contamination event. Utilities developing these water quality monitoring systems are faced with the decision of what locations are best suited for deployment of these sensors to maximize their ability to detect contamination events. TEVA-SPOT is sensor placement software tool that uses an optimization algorithm to evaluate the hydraulic model of a system and recommend optimal locations for water quality sensor deployment. This paper presents a study in which several systems were investigated using the TEVA-SPOT software. A database of distribution system models of small utilities was first created to support this research. Models were then used in the TEVA-SPOT software and input data such as contamination scenario, number of sensors to be deployed, and sensor design objective were then assigned. The locations recommended by the software as optimal sensor locations were evaluated and investigated for patterns as compared to other systems. Each sensor placement recommendation was analyzed to determine the proximity of the sensor locations relative to one another and evaluated in terms of the need for additional sensors. Results of the sensor placement evaluation were grouped by system configuration, and trends were examined based on loop, grid, or branch configuration. The results of this study indicated that 50 to 75 percent of sensor designs had the two sensor locations placed close together for the loop and grid systems, respectively. However, no sensor designs had two sensor locations placed close together in the branch systems. These same trends were reflected in the need for additional sensors.
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1.0 Introduction Water distribution systems are an integral part of society. The availability of clean and dependable supply of water influences both the socioeconomic status and health of a populace. Presidential Decision Directive 63 (Clinton, 1998) identified water infrastructure as one of eight critical infrastructures to be assessed for vulnerability to terrorism. Contamination warning systems (CWS) are proactive strategies to lessen the effects of a contamination event in a water distribution system. One component of CWS, classified as online quality monitoring, involves the network of sensors that can assess the quality of water in the distribution system and alert an operator of a potential contamination event. Utilities developing these water quality monitoring systems are faced with the decision of what locations are best suited for deployment of these sensors. The location of these sensors is a critical component of a CWS. These water quality sensors must be placed in locations that maximize their ability to detect contamination events. To date, there is no applicable federal or state guidance to assist utilities in the deployment of water quality sensors. Distribution systems are complex, dynamic infrastructure that differ greatly for individual utilities. This creates difficulties in the development of general guidance for sensor placement that are applicable to all distribution systems. Technological advancements in sensor placement optimization software may help solve the problem of sensor placement issues for some utilities. The Threat Ensemble Vulnerability Assessment Sensor Placement Optimization Tool (TEVA-SPOT) software has been developed to evaluate distribution systems and offer helpful information and recommendations for water quality sensor placement. TEVA-SPOT requires the input of a calibrated hydraulic model of the system. A model database of small utilities was created for use in this sensor placement optimization software. For purposes of this research, small utilities will be considered to be utilities that service demand loads of less than three million gallons per day. This sensor placement software evaluates the hydraulic model of a system and recommends optimal locations for water quality sensor deployment. The locations recommended by the software as optimal sensor locations will be evaluated and investigated for patterns as compared to other systems. This paper presents the development of the model database of small utilities and the recommended optimal placement of water quality sensors in these systems found from the sensor placement optimization software. The investigation of sensor placement recommendations and potential patterns as compared to other systems will also be presented.
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2.0 Operational Theory for TEVA-SPOT The TEVA-SPOT software provides computational tools to evaluate impacts and optimize sensor placement in a water distribution network to support the design of CWS. The Environmental Protection Agency (EPA) National Homeland Security Research Center developed the TEVA-SPOT program with collaborators from Sandia National Laboratories, Argonne National Laboratory, and the University of Cincinnati (Berry et al., 2010). TEVA-SPOT works by evaluating a model of a distribution system and using an optimization algorithm to determine the best location for a given number of sensors. The software requires inputs regarding the types of contamination scenarios that will be simulated along with the objectives of sensor designs and the number of sensors that can be deployed. TEVA calculates the possible health impacts using probabilistic models of consumer water consumption and responses to contaminant doses. Sensor designs are optimized to minimize these impacts for particular objectives that are selected by the user. TEVA-SPOT has two major components: modeling and decision making. The modeling component describes the hydraulic characteristics of the water distribution system with a model input. This component uses the hydraulic model to simulate a large range of contamination events and to measure the resulting public health impact. The contamination scenario is defined by the amount, duration, and type of contaminant that will be introduced into system. The decision making component determines the optimal sensor placement. Sensor design parameters are defined by number of sensors, the objectives, and constraints of sensor placement. The decision making component works by analyzing tradeoffs of different sensor designs to determine the most optimal design (Murray et al., 2010). TEVA-SPOT software consists of three main software modules that are as follows: simulate incident, assess consequences, and optimize sensor placement. Simulating the incident requires the input file of the utility network, a definition for the contamination event (e.g. duration of contaminant injection, possible injection node locations), and objectives of sensor design (e.g. minimizing the mean population exposed, minimizing the time to detection). The simulation of the incident creates a threat ensemble database for contamination events at the nodes. A threat ensemble database, which is a binary database containing hydraulic information and the concentration matrix, shows the evaluation of how the model responds to the simulated event. The objective of sensor placement will determine how nodes are evaluated for sensor placement. Users can also specify weights to certain nodes in order to capture any information a utility may have about locations that are more likely to be subject to contamination events. The second module assesses the threat ensemble database for consequences of injection. The consequences are evaluated in terms of user defined performance objectives (e.g. number of people made ill, length of pipe contaminated). The consequence assessment module creates an impact file that is assessed by the sensor optimization module to create a sensor location file.
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The calculation of expected-impacts by TEVA uses data stored in the threat ensemble database to estimate impacts. Several factors that create the threat ensemble database influence the expected impacts of contamination. These factors include duration of contamination injection, mass of contaminant injections, distribution of population in network model, and the model used for ingestion of tap water (Davis and Janke, 2010). Impacts represent both health impacts and economic impacts of contamination events. Health impacts include injury, disease, illness, and death (Murray et al., 2005). Economic impacts include all costs related to restoration such as cleanup, treatment, remediation, and decontamination. Economic impacts also include costs for medical treatments such as hospitalization and vaccinations (Panguluri et al., 2005). Impacts represent particular consequences associated with contamination. The value of the impacts of contamination is given by the result of the sensor placement objective function. A flow chart of the TEVA-SPOT software is shown in Figure 1.
Figure 1. Flow Chart of TEVA-SPOT Software (Murray et al., 2010) The inputs of the sensor placement software were consistent for all systems evaluated for this study. Contamination scenarios for which sensor designs were generated were assumed to be reasonable depictions of possible contamination scenarios. Sensor placement designs modeled a feasible number of sensors considering a typical small utilities’ budget. Sensor design objectives also reflected objectives that are considered relevant to the priorities of small utilities. It is important to have an accurate hydraulic model of the system for input into TEVA-SPOT in order to find the best recommendation for optimal sensor placement.
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3.0 Hydraulic Models Used for Study In order to investigate sensor placement recommendations using TEVA-SPOT, a database of hydraulic models that was representative of typical small utilities was first developed. The systems contained within the database were selected based on certain criteria. The first criterion was that systems must deliver between one and three million gallons of water daily. Systems were classified as branch, loop, or grid spatial configuration, based on system configuration and flow dynamics. The systems were also selected to provide a range of different system characteristics such as number of pumps, tanks, and reservoirs. This variance in system characteristics was intended to give researchers a variety of system types on which to test optimization algorithms. Data to develop these system models was gathered from the Water Resource Information System (WRIS) maintained by The Kentucky Infrastructure Authority (KIA), which is a database of Geographic Information System (GIS) shapefiles representing Kentucky’s water distribution system components. All basic model components (pipes, pumps, tanks, sources, and water treatment plants) were overlaid in GIS, and the pipe network analysis software KYPIPE was then used to further build the models. The database consists of twelve hydraulic models. The models were assigned an ID number and were named as KY#. All identifying information for the actual distribution systems modeled was removed for security purposes. Model names were grouped by configuration. The first four models KY1-KY4 were in loop configuration, KY5-KY8 were grid configuration, and KY9-KY12 were classified as branch systems. Table 1 shows general parameters of each system. Figure 2 shows a schematic (in KYPIPE) of three different systems used in the database, representing each configuration type. Table 1. General System Parameters of Database Models. System System No. No. No. Name Configuration Tanks Reservoirs Pumps
KY1 KY2 KY3 KY4 KY5 KY6 KY7 KY8 KY9 KY10 KY11 KY12
Loop Loop Loop Loop Grid Grid Grid Grid Branch Branch Branch Branch
4 2 3 4 3 3 3 5 15 13 28 7
3 1 3 0 4 2 1 2 4 2 1 1
8 1 2 2 13 3 3 10 27 16 25 14
Total System Demand (MGD)
Length of Pipe (mile)
No. Nodes
1.50 2.09 2.02 1.51 2.28 1.56 1.53 2.47 1.38 2.26 1.93 1.38
104 95 58 162 59 77 85 159 606 275 305 404
777 764 266 939 409 513 500 1283 1151 901 785 2276
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Figure 2. System Models Representing each Configuration: (A) KY 1 Loop; (B) KY 5 Grid; (C) KY 9 Branch. All models in the database were uncalibrated due to a lack of information available to perform a network calibration. Every pipe had a default Hazen-Williams roughness value. Although model roughness coefficients may not reflect the true roughness coefficient, it provided a reasonable estimate for research purposes. The automatic demand distribution tool in KYPIPE was used to approximate the spatial allocation of water usage throughout the model. This tool takes the entire demand on a system and distributes it to nodes based on pipe size. For example, pipes with larger diameters receive a greater proportion of the total demand than smaller pipes. 4.0 TEVA-SPOT Analysis Using the hydraulic model database, a main objective of this research was to evaluate all systems with the sensor placement software. The contamination scenario that was simulated at every non-zero demand node was the injection of a chemical contaminant at a rate of 800 milligrams per minute over 2 hours, introducing a total of
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96 grams of chemical contaminant into the water supply. Contamination scenarios considered all non-zero demand nodes as possible contamination injection sites. Sensor designs were generated for design objectives that are relevant to the priorities of small utilities. The designs only considered the placement of two sensors, a feasible number of sensors considering a typical budget of a small utility. Sensor designs minimized the mean time to detection and considered all non-zero demand nodes as feasible sensor locations. An analysis was performed on each sensor location recommended by TEVA-SPOT. Sensor designs for all twelve systems were evaluated considering the macro-level placement of the sensor in relation to the rest of the systems’ pipe configuration, source tanks and reservoirs. Sensor design evaluations considered the placement of the two sensors relative to each other. Relative locations were defined in three terms: close, opposite sides of the system, and adjacent but different portions of the system. The performance of each sensor design was also evaluated. The percent of events witnessed and areas of event coverage were determined visually from TEVA-SPOT sensor performance maps. Figure 3 displays the output from TEVA (for KY5) showing the performance of each sensor, specifically witnessed and unwitnessed contamination events at nodes throughout the system. Performance of the sensor was evaluated by the need for additional sensors to provide adequate event coverage for the system.
Figure 3. KY5 Sensor Performance. Table 2 shows the evaluation of sensor placement, in relation to the rest of the system and to each other, along with the effectiveness of sensor placement in witnessing contamination events for each system. Table 3 summarizes the need for additional sensors and sensor location relative to each other based on system configuration. The analysis shows that additional sensors were needed in 100% of branch configuration models and 50% of the loop systems, while 0% of the grid systems needed additional sensors to provide adequate coverage.
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Table 2. Summary of Sensor Placement and Performance. System Configuration Name Type
Relative Geographic Sensor Placement Opposite sides of system
Portion of Events Witnessed Slightly more than half
KY1
Loop
KY2
Loop
Close
Half
KY3
Loop
Almost all
KY4
Loop
KY5
Grid
KY6 KY7
Grid Grid
Close Opposite sides of system Opposite sides of system Close Close
KY8
Grid
Close
Majority
KY9
Branch
KY10
Branch
KY11
Branch
KY12
Branch
Adjacent, but different portions of system Adjacent, but different portions of system Opposite sides of system Adjacent, but different portions of system
Areas of Coverage
Possible Need for Additional Sensors
Central
Yes
Majority Majority Majority Majority
Central, right All Central, right Central, Upper Central Central Central, Lower
Yes No No No No No No
25 percent
Central, Lower
Yes
Less than 25 percent
Central, Right
Yes
Slightly less than half
Central, Lower
Yes
Slightly less than half
Central, Upper
Yes
Table 3. Summary of Sensor Placement and Performance by Configuration. System Name KY1-KY4 KY5-KY8 KY9-KY12 All
Configuration Type Loop Grid Branch All
Percent with Sensors Located Close Together 50% 75% 0% 41.7%
Percent Needing Additional Sensors 50% 0% 100% 50%
Individual sensor locations were analyzed in terms of location in the system (interior or outside) and proximity to a filling tank. Sensor locations were evaluated in terms of betweenness centrality and receivability. Betweenness centrality defines the centrality of a node based on the degree to which a node falls on the shortest path between other node pairs. Betweenness centrality for each node can be calculated by a graph search algorithm, but for this research was determined by visual judgment algorithm (Xu et al., 2008). Receivability is a measure of how many other nodes are upstream of a particular node. Values to define receivability for every node can be determined by a search algorithm using a directional flow graph, but for this research was determined by visual judgment of how many nodes for which the sensor location
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will serve as a witness. Table 4 shows the evaluation of sensor placement based on these parameters. Results show that grid systems had the highest percentage of sensors located inside the system and in densely populated regions, while branch systems had the lowest percentages in these criterions. Table 4. Summary of Percentage for Individual Sensor Locations. System Name
Configuration Type
KY1-KY4 KY5-KY8 KY9-KY12 All
Loop Grid Branch All
Percent Located on Inside of System 25% 37.5% 0% 20.8%
Percent Located in Densely Populated Node Region 50% 62.5% 25% 45.8%
Percent Located Near Filling Tank 25% 12.5% 37.5% 25%
Nodes were evaluated in terms of receivability to central supply lines. In loop and branch systems, there is a central supply line that provides flow to the center of the system that is dispersed outwards. In grid systems, large supply lines run on the outside of the system then disperse flow inwards. Nodes downstream of these large supply lines receive flow from many upstream nodes. This makes these locations excellent sensor locations because they can serve as witnesses for any contamination events that may occur at upstream nodes. Sensor locations were also evaluated in terms of receivability to tanks and sources. A sensor location can be receivable to tanks and sources, but not necessarily be downstream of a large supply line. This is particularly true in branch systems which have several smaller sources. Table 5 shows sensor placement recommendations analyzed by these parameters. Results show that 100% of sensors were downstream of sources and downstream of many other nodes in all system configuration types. Branch systems resulted in the largest percentage of sensors located downstream of a large supply line. Table 5. Summary of Receivability for Individual Sensor Locations. System Name
Configuration Type
KY1-KY4 KY5-KY8 KY9-KY12 All
Loop Grid Branch All
Percent Located Downstream of Large Supply Line 37.5% 37.5% 62.5% 45.8%
Percent Located Downstream of Sources 100% 100% 100% 100%
Percent Receivable to Many Nodes 100% 100% 100% 100%
5.0 Summary and Conclusions Software has been developed to evaluate hydraulic models of a WDS and make recommendations for optimal water quality sensor placement. Hydraulic models of small distribution systems were developed and used in the TEVA-SPOT software, utilizing input data such as contamination scenario, number of sensors to be deployed,
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and sensor design objective. However, many smaller utilities do not have hydraulic models of their system, as hydraulic model development and calibration is a complex and expensive process that is simply not feasible for many small utilities. These small utilities need access to sensor placement guidance to assist them in their deployment of water quality sensors into their distribution systems. This research provides a foundation for future research to develop sensor placement guidance. Once established models are evaluated by sensor placement software, the recommended sensor deployment locations can be studied to determine if patterns of optimal sensor locations exist in similar systems. If these patterns are observed and confirmed, guidance for small utilities can be developed to offer small utilities assistance in water quality sensor placement without the need for a calibrated hydraulic model. Acknowledgement Funding for this research was provided by the U.S. Department of Homeland Security, Science and Technology Directorate, through a technology development and deployment program managed by The National Institute for Hometown Security, under an Other Transactions Agreement, OTA #HSHQDC-07-3-00005, Subcontract #02-10-UK. This support was greatly appreciated. 6.0 References Berry, J. W., Boman, E., Riesen, L. A., Hart, W. E., Phillips, C. A., and Watson, J.-P. (2010). User’s Manual: TEVA-SPOT Graphical User Interface User’s Manual, EPA-600-R-08-147, U.S. Environmental Protection Agency, Office of Research and Development, National Homeland Security Research Center, Cincinnati, OH. Clinton (1998). “Policy on Critical Infrastructure Protection.” Presidential Decision Directive 63 (PDD-63). (May 22 1998). Davis, M. and Janke, R. (2010). “Patterns in potential impacts associated with contamination events in water distribution systems.” Journal of Water Resources Planning and Management, 134(5), 449–456 Murray, R. , Haxton, T. , Janke, R. , Hart, W.E. , Berry, J., and Phillips, C.A. (2010). “Sensor Network Design for Drinking Water Contamination Warning Systems.” EPA 600-R-09-141, U.S. Environmental Protection Agency, Office of Research and Development, National Homeland Security Research Center, Cincinnati, OH. Murray, R., Janke, R., and Uber, J. (2005) “The Threat Ensemble Vulnerability Assessment Program for Drinking Water Distribution System Security.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA. May 15-19, 2005. Panguluri, S., Grayman, W. M. , and Clark, R. M. (2005). “A Reference Guide for
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Utilities.” EPA 600-R-06-028, U.S. Environmental Protection Agency, Office of Research and Development, National Homeland Security Research Center, Cincinnati, OH. Xu, J. M., Fischbeck, P. S., Small, M. J., VanBriesen, M., Casman, E. (2008). “Identifying Sets of Key Nodes for Placing Sensors in Dynamic Water Distribution Networks.” Journal of Water Resources Planning and Management, 134(4), 378–385.
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