Sensor Placement Guidance in Small Water Distribution Systems25.3] Comparison of KYPIPE... · Sensor Placement Guidance in Small Water Distribution Systems Developed by the University

Sensor Placement Guidance in Small Water Distribution Systems

Developed by the University of Kentucky and KYPIPE LLC

Prepared for the

National Institute for Hometown Security

368 N. Hwy 27

Somerset, KY 42503

June 31, 2013

This research was funded through funds provided by the U.S. Department of Homeland Security,

administered by the National Institute for Hometown Security, under an Other Transactions

Agreement, OTA #HSHQDC-07-3-00005, Subcontract #02-10-UK.

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TABLE OF CONTENTS

TABLE OF CONTENTS ................................................................................................................. i

LIST OF TABLES ......................................................................................................................... iv

LIST OF FIGURES ........................................................................................................................ v

CHAPTER 1 ................................................................................................................................... 1

1 Introduction .......................................................................................................................... 1

1.1 Background ................................................................................................................... 1

1.2 Objectives of Study ...................................................................................................... 2

1.3 Content of Report ......................................................................................................... 4

CHAPTER 2 ................................................................................................................................... 6

2 Sensor Placement in Water Distribution Systems ............................................................... 6

2.1 TEVA-SPOT ................................................................................................................ 6

2.1.1 TEVA-SPOT Methodology ...................................................................................... 6

2.1.2 TEVA-SPOT Case Study .......................................................................................... 9

2.2 KYPIPE ...................................................................................................................... 14

2.2.1 KYPIPE Methodology ............................................................................................ 14

2.2.2 KYPIPE Case Study ............................................................................................... 19

2.3 Current Trends in Sensor Placement .......................................................................... 22

2.3.1 Betweenness Centrality and Receivability .............................................................. 24

2.3.2 Rule-Based Expert System ..................................................................................... 28

2.3.3 Rule-Based Decision Support System .................................................................... 30

2.3.4 Demand and Reachability ....................................................................................... 32

CHAPTER 3 ................................................................................................................................. 36

3 Water Distribution System Models .................................................................................... 36

3.1 System Configurations ............................................................................................... 36

3.2 General Procedures of Model Development .............................................................. 40

3.2.1 Pipe Roughness Coefficients .................................................................................. 42

3.2.2 Model Demand Input .............................................................................................. 43

3.2.3 Final Adjustments to Model.................................................................................... 45

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3.3 Description of Models used in Study ......................................................................... 46

3.4 Steady State and EPS Simulations .............................................................................. 50

CHAPTER 4 ................................................................................................................................. 52

4 KYPIPE Tool Sensor Placement Analysis ........................................................................ 52

4.1 Theory ......................................................................................................................... 52

4.2 Procedure for Execution ............................................................................................. 55

4.3 Performance Evaluation ............................................................................................. 56

4.3.1 Contamination Scenarios ........................................................................................ 56

4.3.2 Time to Detection Comparison ............................................................................... 58

4.3.3 Comparison of Identical Sensor Placement ............................................................ 63

4.3.4 Results for All Contamination Scenarios ................................................................ 68

CHAPTER 5 ................................................................................................................................. 71

5 Conclusion ......................................................................................................................... 71

5.1 KYPIPE Sensor Placement Tool Conclusion ............................................................. 71

5.2 Comparison of Results between KYPIPE and TEVA-SPOT Conclusion ................. 72

CHAPTER 6 ................................................................................................................................. 75

6 Acknowledgements ............................................................................................................ 75

CHAPTER 7 ................................................................................................................................. 76

7 References .......................................................................................................................... 76

Appendix A ................................................................................................................................... 79

Appendix B ................................................................................................................................... 83

B.1 Data Acquisition in GIS ................................................................................................. 84

B.2 Data Input to KYPIPE .................................................................................................... 87

B.3 Addition of Elevation Data ............................................................................................ 90

B.4 Final Adjustments to Model ........................................................................................... 99

Appendix C ................................................................................................................................. 105

Appendix D ................................................................................................................................. 114

D.1 Execution of Tool ......................................................................................................... 115

D.2 Sensor Placement Tool in Progress .............................................................................. 119

D.3 Results Provided by Tool ............................................................................................. 123

Appendix E ................................................................................................................................. 128

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E.1 Results for Placement of One Sensor ........................................................................... 129

E.2 Results for Placement of Two Sensors ......................................................................... 134

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LIST OF TABLES

Table 1: System Characteristics .................................................................................................... 50

Table 2: Contamination Scenarios ................................................................................................ 57

Table 3: Comparison between KYPIPE and TEVA-SPOT for Baseline Conditions ................... 59

Table 4: Analysis of Faster Times to Detection between KYPIPE and TEVA-SPOT ................. 62

Table 5: Identical Sensor Selection between KYPIPE and TEVA-SPOT (1 sensor) ................... 64

Table 6: Identical Sensor Selection between KYPIPE and TEVA-SPOT (2 sensors) ................. 67

Table 7: Sensor Placement Results for KY 1 (1 sensor) ............................................................... 69

Table 8: Sensor Placement Results for KY 1 (2 sensors) ............................................................. 70

Table 9: Attribute Matching between GIS and KYPIPE .............................................................. 90

Table 10: Hazen-Williams Roughness Coefficients ................................................................... 101

Table 11: KYPIPE and TEVA-SPOT Sensor Placement Results (1 sensor).............................. 129

Table 12: KYPIPE and TEVA-SPOT Sensor Placement Results (2 sensors) ............................ 134

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LIST OF FIGURES

Figure 1: Flow Chart of TEVA-SPOT Software (Murray et al., 2010) .......................................... 7

Figure 2: Sensor Placement at BWSN – Network 1 (Ostfeld et al., 2008) ................................... 11

Figure 3: Objective Comparisons – Network 1 (Ostfeld et al., 2008) .......................................... 12

Figure 4: Objective Comparisons – Network 2 (Ostfeld et al., 2008) .......................................... 13

Figure 5: Clustering of Nodes with High Betweenness Centrality (Xu et al., 2008) ................... 24

Figure 6: Community Divide in a WDS (Xu et al., 2008) ............................................................ 25

Figure 7: Selected Nodes within each Community (Xu et al., 2008) ........................................... 26

Figure 8: Sensor Placement based on Receivability (Xu et al., 2008) .......................................... 27

Figure 9: Inner Nodes and Path Nodes (Chang et al., 2011) ........................................................ 29

Figure 10: General Procedure for RBDSS (Chang et al., 2012a). ................................................ 32

Figure 11: System Configurations (a) Loop, (b) Grid, (c) Branch (taken Gagliardi and

Liberatore)..................................................................................................................................... 37

Figure 12: System in Branch Configuration ................................................................................. 38

Figure 13: System in Loop Configuration .................................................................................... 39

Figure 14: System in Grid Configuration ..................................................................................... 40

Figure 15: Water Line Shapefile. .................................................................................................. 41

Figure 16: Model Development Procedure. .................................................................................. 42

Figure 17: Systems in Loop Configuration: (A) KY1; (B)KY2; (C) KY3; (D) KY4; (E) KY13 47

Figure 18: Systems in Grid Configuration: (A) KY5; (B) KY6; (C) KY8; (D) KY14; (E) KY7 48

Figure 19: Systems in Branch Configuration: (A) KY9; (B) KY10; (C) KY11; (D) KY12; (E)

KY15 ............................................................................................................................................. 49

Figure 20: Sensor Placement Tool Theory (Minimum Travel Time) ........................................... 53

Figure 21: Sensor Placement Tool Theory (Average Travel Time) ............................................. 54

Figure 22: Sensor Placement Tool Flowchart. .............................................................................. 56

Figure 23: Comparison between KYPIPE and TEVA-SPOT - Baseline Conditions (1 sensor) .. 60

Figure 24: Comparison between KYPIPE and TEVA-SPOT - Baseline Conditions (2 sensors) 61

Figure 25: Identical Sensor Selection between KYPIPE and TEVA-SPOT (1 sensor) ............... 64

Figure 26: Example of Differing Sensor Placement in Close Proximity ...................................... 65

Figure 27: Example of Differing Sensor Placement not in Close Proximity ................................ 66

Figure 28: Identical Sensor Selection between KYPIPE and TEVA-SPOT (2 sensors) .............. 67

Figure 29: Water Utility Poster (Determining Water Distribution System Configuration) .......... 81

Figure 30: Water Utility Poster (Procedure for Executing KYPIPE Sensor Placement Tool) ..... 82

Figure 31: Distribution System Component Shapefiles in GIS .................................................... 84

Figure 32: Select by Attributes ..................................................................................................... 85

Figure 33: Calculating Length of Water Lines ............................................................................. 86

Figure 34: Data Export in GIS ...................................................................................................... 87

Figure 35: Matching Attributes between GIS and KYPIPE (Pipes) ............................................. 88

Figure 36: Matching Attributes between GIS and KYPIPE (Pumps)........................................... 89

Figure 37: NRCS Geospatial Data Gateway ................................................................................. 91

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Figure 38: KYPIPE Nodes Shapefile Export ................................................................................ 92

Figure 39: Nodes Shapefile in GIS ............................................................................................... 93

Figure 40: Digital Elevation Model .............................................................................................. 94

Figure 41: Combined DEM Process ............................................................................................. 95

Figure 42: Elevation Extraction Process ....................................................................................... 96

Figure 43: Opening Elevation File in Excel ................................................................................. 97

Figure 44: Elevation Data Sorting in Excel .................................................................................. 98

Figure 45: Nodal Elevation Data in Excel .................................................................................... 99

Figure 46: Pipe Connection Errors ............................................................................................. 100

Figure 47: Changing Roughness Values of Pipes in KYPIPE .................................................... 101

Figure 48: Demand Allocation in KYPIPE ................................................................................ 102

Figure 49: Demand Patterns in KYPIPE .................................................................................... 103

Figure 50: Control Switches in KYPIPE .................................................................................... 104

Figure 51: KY 1 System Layout ................................................................................................. 106









Figure 60: KY 10 System Layout ............................................................................................... 111






Figure 66: EPS Setup in KYPIPE ............................................................................................... 115

Figure 67: Execution of EPS Analysis........................................................................................ 116

Figure 68: Starting Sensor Placement Tool ................................................................................ 117

Figure 69: Setting Parameters in Sensor Placement Tool ........................................................... 118

Figure 70: Initiating Sensor Placement Run ............................................................................... 119

Figure 71: Sensor Placement Tool in Progress ........................................................................... 120

Figure 72: Sensor Selection Process ........................................................................................... 121

Figure 73: Completion of Sensor Placement Simulation ............................................................ 121

Figure 74: Completed Sensor Placement Simulation ................................................................. 122

Figure 75: Completed Sensor Placement Tool Display .............................................................. 123

Figure 76: Turning on Node Labels ............................................................................................ 124

Figure 77: Displaying Selected Nodes on the Map .................................................................... 124

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Figure 78: Sensor Placement Summary Report .......................................................................... 125

Figure 79: WQC File .................................................................................................................. 126

Figure 80: Time Matrix Excel File ............................................................................................. 127

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CHAPTER 1

1 Introduction

1.1 Background

Water distribution systems are an integral part of society, and the availability of a clean

and dependable supply of water influences both the socioeconomic status and health of a

populace. In recent years, protecting the nation’s critical infrastructure from terrorist

attacks has become a priority, and efforts to protect the water infrastructure are included in

this goal. Water distribution systems are considered to be vulnerable to intentional, along

with accidental, contamination because they have a large spatial distribution and multiple

points of access. Many systems lack monitoring and security systems, greatly increasing

the risk and potential danger associated with an attack (Hart and Murray, 2010).

Public awareness of this threat has also increased from media coverage of two international

terrorist plots against drinking water systems. One attack planned to introduce a cyanide

compound into water lines near a U.S. Embassy in Italy, and another was a direct threat

from an Al-Qaeda operative to American water supplies (Murray et al., 2010). Threats to

the nation’s water supply are concerning because they can cause a significant negative

impact to public health and the economy in a short amount of time. Possible terrorist

attacks to water supplies include sabotage of Supervisory Control and Data Acquisition

(SCADA) systems, the physical destruction of facilities, airborne release of hazardous

chemicals onsite, and the injection of chemical, biological, or radiological contaminants

into the water supply. The threat of contaminant injection is perhaps the most dangerous

because of the major public health, economic, and psychological impacts that could result

(Murray et al., 2010).

Intentional contamination of water distribution systems has become an increasing concern

in recent years, but the accidental contamination of drinking water is also possible.

Humans can unintentionally contamination distribution systems with pesticides, toxic

industrial chemicals, or other materials. Systems can also be contaminated if metals,

organic contaminants, or asbestos in pipe materials and linings are able to leach into the

network. The risk of soil and groundwater contaminants permeating through plastic pipes

is also present. Pesticides, insecticides, or other chemicals are able to enter the system

2

through accidental backflow occurrences or breaks in pipes/leaky joints (Murray et al.,

2010). In an effort to mitigate the risks from intentional or accidental contamination of the

water supply, contamination warning systems have been proposed as a cost-effective and

reliable strategy.

Contamination warning systems (CWS) are proactive strategies to lessen the effects of a

contamination event in a water distribution system. The goal of a CWS is to deliver an

early indication of intentional or accidental contamination in order to reduce public health

impacts and economic loss, and it also works to improve the water utility’s capability for a

quick response (Janke et al., 2006). A CWS includes deployment and operation of online

sensors, other surveillance systems, fast communication technology, and data analysis

procedures to provide early alert of a contamination event (Murray et al., 2010). Arguably

the most critical 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 and provide the greatest protection of human health

(McKenna et al., 2006).

1.2 Objectives of Study

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 infrastructures 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 TEVA-SPOT software (Threat Ensemble Vulnerability Assessment Sensor Placement

Optimization Tool) has been developed to analyze the vulnerability of drinking water

distribution networks and aid utilities in the design of sensor networks. A hydraulic model

is setup in EPANET, and this is used as input for TEVA-SPOT to recommend sensor

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placement based on a variety of user defined objectives. However, this software does not

have the ease of use and simplicity that is needed to be beneficial to small utilities. It

requires the use of complex water quality models along with sophisticated optimization

methods to perform sensor placement guidance. Many small, or even medium sized,

utilities might not have the technical or financial resources that are needed to effectively

use TEVA-SPOT. Because of deficiencies in the current resources, the following research

objectives were established:

Develop sensor placement tool in KYPIPE as a simple tool to aid small utilities in

sensor placement

Execute new KYPIPE sensor placement tool on model database of 12 small water

distribution systems for a variety of contamination scenarios

Use TEVA-SPOT to run sensor placement simulations on the same models and

contamination scenarios

Compare results given by KYPIPE and TEVA-SPOT to verify the effectiveness of

the new sensor placement tool

The KYPIPE software is already useful in allowing utility managers to gain a better

understanding of the flow dynamics and overall behavior of their distribution system. The

program is able to complete a hydraulic analysis for any configuration of pipes including

hydraulic components such as pumps, valves, fittings, and storage tanks. The program can

also execute an extended period simulation (EPS) to account for variation over time such

as changes in storage tanks levels and varying pump schedules. The Water Quality (WQ)

sensor placement tool can be used within the KYPIPE program to offer helpful information

and recommendations for water quality sensor placement. The tool will recommend

optimal locations for online sensors based on simple water quality analyses and heuristic

methods that require very little or no added input from utilities. The tool has been

developed to work with the existing KYPIPE graphical user interface. The goal is to

provide a simple tool to aid utility managers in the optimal placement of sensors within

their distribution systems. Optimal placement of water quality sensors will allow

contamination events to be quickly detected, minimizing the negative events of a

contamination event.

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The newly developed sensor placement tool is tested on a database of 12 distribution

system models that are considered small utilities. The tool is executed with a variety of

contamination scenarios for all systems, placing a number of sensors reasonable for the

budget of a small utility. The same scenarios are also executed using the TEVA-SPOT

software, both programs operating with the objective of minimizing the time required to

detect the contaminant based on the given contaminations scenario. The results from both

programs for all executions are compared in order to verify the effectiveness of the new

KYPIPE sensor placement tool.

This research provides the foundation for future work in developing sensor placement

guidance. The recommended sensor locations from the KYPIPE sensor placement tool can

be analyzed to determine if patterns exist based on system characteristics. If trends in the

optimal sensor locations can be observed, guidance can be developed to offer small utilities

assistance in placing water quality sensors. Sensor placement guidance without the need

for a calibrated hydraulic model can be beneficial to the limited resources and budget of a

small utility.

1.3 Content of Report

Chapter 2 presents a technical background on topics to support the contents of this research.

These topics include the methodology behind TEVA-SPOT and KYPIPE and current

research in the area of sensor placement guidance. Chapter 3 presents the database of water

distribution system models created to support this research. A general overview of model

development is presented, along with details of all systems used in the model database.

Chapter 4 presents the KYPIPE sensor placement tool created to provide simple sensor

placement guidance to small utilities. The chapter outlines the methodology behind the

sensor placement tool and results from the execution of both TEVA-SPOT and KYPIPE on

the systems in the model database. The sensor placement results using the two programs are

compared to verify the effectiveness of the new tool in KYPIPE. Chapter 5 presents

conclusions of this research, including an overall summary of the sensor placement tool and

results found using the tool in KYPIPE.

Several appendix sections are also included in this report. Appendix A presents visual tools

to aid utilities in identifying the system configuration of their water distribution network

5

and in executing the sensor placement tool. Appendix B outlines a procedure for developing

models of distribution systems in KYPIPE. Appendix C presents a detailed layout of each

system in the model database. Appendix D outlines the procedure for executing the sensor

placement tool in KYPIPE, and Appendix E contains results for all simulations run with the

sensor placement tool.

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CHAPTER 2

2 Sensor Placement in Water Distribution Systems

2.1 TEVA-SPOT

2.1.1 TEVA-SPOT Methodology

With the increased risk of contamination of water distribution systems through intentional

terrorist activities or accidental occurrences, a methodology was needed that was able to

effectively reflect the vulnerabilities of such systems to all forms of contamination. To

meet these needs, the Threat Ensemble Vulnerability Assessment Sensor Placement

Optimization Tool (TEVA-SPOT) Program was developed as a probabilistic framework

for analyzing the vulnerability of drinking water distribution networks (Murray et al.,

2004). This collection of software tools to aid utilities in the design of sensor networks was

developed by researchers from the Environmental Protection Agency (EPA), Sandia

National Laboratories, the University of Cincinnati, and Argonne National Laboratory

(Murray et al., 2010). TEVA-SPOT creates a threat ensemble, consisting of a set of

contamination scenarios, and the vulnerability of the network is assessed using the entire

threat ensemble (Murray et al., 2004).

TEVA-SPOT contains three main software modules. The first module simulates the set of

incidents in the threat ensemble, the second module calculates the potential consequences

of each incident in the threat ensemble, and the third module optimizes for sensor

placement. The design basis threat consists of the set of incidents for the sensor network to

detect. The consequences are calculated based on one or more of the performance

objectives (people made ill, length of pipe contaminated, etc.). When TEVA places

sensors, the mean consequence for a given objective is minimized. The mean consequences

are averaged over the ensemble of contamination incidents. Minimizing the value is

equivalent to assuming that each contamination scenario is equally likely to occur and that

each is important when selecting sensor locations. The user is able to specify weights to

put more importance on locations with a higher likelihood of being contaminated (Murray

et al., 2010). A flow chart of the TEVA-SPOT software is shown in Figure 1.

7

Figure 1: Flow Chart of TEVA-SPOT Software (Murray et al., 2010)

TEVA uses simulation and optimization models to select optimal placement of sensors for

a contamination warning system by implementing two steps: modeling and decision-

making. The modeling process first involves creating a network model for a hydraulic and

water quality analysis. The modeling process also must include the following steps:

describing sensor characteristics, defining the design basis threat, setting up performance

measures, defining utility response to sensor detection events, and finally identifying

potential sensor locations (Murray et al., 2006). The decision making process uses an

optimization method and evaluates sensor placement; this step is performed by analyzing

trade-offs and comparing a set of designs to account for modeling and data uncertainties

(Murray et al., 2008).

The first step in the modeling process, developing a network model as input to a hydraulic

and water quality modeling software, is critical. In many cases, system models are

developed by utilities to aid in planning, designing new components, and fixing water

quality or hydraulic problems. Using models for the purpose of contamination warning

systems requires a high degree of accuracy; models should be up-to-date and include all

network components, also ensuring they are accurately represented. Characteristics of the

8

sensor behavior are also needed to measure performance of a CWS, so the sensor type,

detection limit, and accuracy should be included (Murray et al., 2008). An assumption

commonly used is an ideal or perfect sensor that is 100 percent reliable. This assumption in

unrealistic, but it is useful in determining an upper bound on performance. A more realistic

input is to assume a detection limit for sensors. The sensor is 100 percent reliable detecting

a contaminant above a certain concentration, and the sensor will fail to detect the

contaminant if it is below the concentration (Murray et al., 2006).

The design basis threat describes the type of threat that the utility wants to protect against

when designing a contamination warning system. Contamination incidents are described

by the specific contaminant, the quantity and duration of injection of the contaminant, and

the locations where the contaminant is introduced. The program understands that these

conditions cannot be known before an incident occurs, so the modeling process takes this

uncertainty into account. The program automatically assumes that each possible injection

location is equally likely to be used to inject the contaminant. An ensemble of incidents is

then simulated, and sensor network designs are chosen based off how they perform for the

entire ensemble of incidents (Murray et al., 2008).

TEVA measures performance of sensor network designs based on minimizing certain

performance objectives such as the number of people who become ill from exposure to a

contaminant, percentage of incidents detected, time to detection, or length of pipe

contaminated. Other objectives like costs or economic impacts can also be used. If a utility

has several important priorities for the performance of their sensor design, multiple

objectives can be considered by assigning a relative importance, or weight, to each

objective (Murray et al., 2010).

Modeling the utility response to contamination events is another important aspect of the

modeling process. Response time is defined as the time between initial detection of the

contaminant and effective warning of the population. This time includes credibility of the

detection, verification of the contaminant presence, and public warning, and this time

period is usually considered to be between 0 and 48 hours (Murray et al., 2010).

9

When selecting nodes for potential sensor locations, certain requirements are needed such

as accessibility, security, and protection from the environment. Obvious locations that

satisfy all requirements are utility owned locations such as pumping stations, tanks, valve

stations, etc. However, other locations could be easily adapted to meet requirements, such

as fire/police stations, schools, city buildings, etc. Even consumer connections could be

adapted to meet sensor location needs, although securing access to private homes or

businesses could be problematic. However, a longer list of feasible sensor sites results in a

sensor design that is more likely to perform well. So the benefits of using sites that need

some adaptation to meet requirements may be worth the additional costs (Murray et al.,

2008).

The second main step in the TEVA sensor placement framework is the decision process.

The goal of this step is to aid utilities in understanding the public health and cost tradeoffs

between different sensor placement designs and ultimately help them choose the sensor

design that will best meet their needs. This is accomplished by using an incremental

approach for applying optimization to generate a set of sensor placement designs. The first

sensor placement design is found under ideal conditions with simplifying conditions. The

assumptions are then removed one at a time to make the designs more realistic. After every

iteration, the performance of the new sensor design is compared with the previous designs

to understand what has been gained or lost with each assumption (Murray et al., 2010).

The decision making step uses the contamination warning system model to evaluate a

series of sensor network designs in a systematic way (Murray et al., 2008).

The TEVA-SPOT software allows a utility to achieve objectives important to their needs

by optimizing the contamination warning system. Sensor locations are chosen based on the

given performance characteristics, likely utility response times, and performance measures

important to the utility. The sensor locations will be designed to protect against a variety of

contamination threats, and the locations can be restricted to locations preferred by the

utility (Murray et al., 2008).

2.1.2 TEVA-SPOT Case Study

The Battle of the Water Sensors Network (BWSN) was held at the Eighth Annual Water

Distribution Systems Analysis Symposium in Cincinnati, OH on August 27-29, 2006. The

10

goal of the BWSN was to compare the performance of 15 sensor network designs that were

applied to two water distribution systems. The sensor network designs were evaluated with

four design objectives: expected time of detection, population affected, consumption of

contaminated water, and detection likelihood. The expected time to detection, the elapsed

time from the start of contamination to the first identified presence of a nonzero

contaminant concentration, will be the focus of this case study (Ostfeld et al., 2008).

Participants were asked to provide designs for selecting the optimal placement of five

sensors and 20 sensors for a base case and three derivative cases. This case study will

strictly focus on the placement of five sensors for the base case. Some characteristics of the

base case include an injection flowrate of 125 L/h, contaminant concentration of 230,000

mg/L, and injection duration of 2 hours. Each contamination scenario utilized a single

injection location, occurring at any injection node with equal probability. Sensors were

able to instantly detect nonzero contaminant concentrations and actions were taken to

eliminate further exposure with no delay (Ostfeld et al., 2008).

15 different sensor designs were examined at the BWSN. Berry et al. submitted a p-median

formulation to define the sensor placement problem, and this was further solved using a

heuristic method. This method reflects the procedure executed in TEVA-SPOT. This

sensor placement algorithm, along with the 14 other methods submitted to the BWSN, was

tested with two water distribution systems. Network 1 consisted of 126 nodes, two tanks,

168 pipes, two pumps, and one constant head source. Network 2 was comprised of 12,523

nodes, two tanks, 14,822 pipes, four pumps, and two constant head sources. During the

BWSN, it was determined that the sensor placement problem was actually multi-objective,

so a unique single optimal solution could not be identified (Ostfeld et al., 2008). However,

each algorithm was still evaluated with the four objective functions, and the results are

outlined. Figure 2 displays the selected sensor locations using all 15 methods for Network

1, Case A (time to detection), and placement of five sensors. The method used in TEVA-

SPOT is labeled using the red triangle (also labeled Berry et al.). Figure 2 shows that three

of the five sensors selected by Berry et al. were common to at least three other methods.

11

Figure 2: Sensor Placement at BWSN – Network 1 (Ostfeld et al., 2008)

It was desired to minimize the time to detection while selecting optimal sensor locations, so

the time to detection for the chosen sensors were compared among all 15 sensor designs.

Figure 3 shows the first three objectives (all compared to the detection likelihood) from the

15 sensor designs for the Network 1, five sensor scenario. The objective labeled Z1

represents the time to detection objective. The method used in TEVA-SPOT, labeled as

point 1, is in the lower half of all 15 sensor designs in terms of the goal to minimize the

first three objectives (time to detection, population affected, and consumption of

contaminated water).

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Figure 3: Objective Comparisons – Network 1 (Ostfeld et al., 2008)

The same procedure was also executed to compare sensor designs for Network 2. Optimal

sensor locations were chosen using all 15 design algorithms, and the results were compared

in terms of the design objectives. Figure 4 shows the comparisons of the first three

objectives (also in relation to the detection likelihood objective) for Network 2 and the

placement of five sensors. Because it is desired to minimize the first three objectives,

Figure 4 shows that the algorithm developed by Berry et al. performs very favorably. The

method used in TEVA-SPOT resulted in the lowest values for the first three objectives,

although it is equivalent to results using the method proposed by Krause et al. These results

prove that the method developed by Berry et al. is effective in selecting sensor locations

that minimize time to detection, population affected, and consumption of contaminated

water.

13

Figure 4: Objective Comparisons – Network 2 (Ostfeld et al., 2008)

Although these results show the effectiveness of the sensor placement algorithm proposed

by Berry et al., an important point should also be noted. In a water distribution system,

there are many injection points on the exterior of the system that lead to small pipes that do

not propagate throughout the system. If a contaminant is injected at these nodes, it may

never be detected. If optimization is performed to minimize time to detection taking into

account non-detections, it is desired to avoid non-detections as much as possible. This

results in placement of sensors far from the center of the network in order to maximize

detection, which is opposite the intuitive approach for minimizing time to detection. To

avoid this non-intuitive behavior, it was decided at the BWSN not to include non-

detections when looking at the time to detection objective.

14

2.2 KYPIPE

2.2.1 KYPIPE Methodology

KYPIPE was first developed to calculate steady state flows and pressures in a water

distribution system. The program is able to complete an analysis for any configuration of

pipes including hydraulic components such as pumps, valves, fittings with significant head

losses, flow meters, and storage tanks. The program also has the capabilities to execute an

extended period simulation (EPS). This extended simulation can account for the variation

in storage tanks levels over time, even controlling the open-closed status of a pipe or pump

based on the water level in a tank or hydraulic grade at another location. In addition to

calculating the flow in each pipe and pressure at each node for a certain operating

condition, KYPIPE can also calculate certain design, operation, and calibration parameters

to meet specified pressure requirements. Some of these parameters include pump

speed/power, HGL setting for a storage tank, control valve settings, pipe diameters, etc.

(Wood, 2010).

In order to execute a hydraulic simulation of a system, the user must enter data to describe

the pipes, pumps, minor loss components, valves, storage tanks, pressure switches, etc. For

example, parameters that must be input for all pipes include length, inside diameter, and

roughness coefficient. KYPIPE then analyzes the distribution system by solving the set of

mass continuity and energy equations using linearization methods to handle the nonlinear

terms. After extensive testing of different algorithms for analysis of pipe systems, the

creators of KYPIPE determined that the approach used in the program is the most powerful

and has the best convergence of all approaches (Wood, 2010). The methods used by

KYPIPE to solve water distribution systems are outlined in this section.

The relationship between the number of pipes, junction nodes, fixed-grade nodes, and

loops can be helpful in understanding the methods of solving water distribution systems.

Equation 1 is held true for a distribution system, and it can be related to the basic hydraulic

equations to describe steady state flow in a system (Wood, 1981).

1 fljp (1)

15

where p represents the number of pipes, j is the number of junction nodes, l signifies the

number of primary loops, and f is the number of fixed-grade nodes. Nodes are divided into

two different classifications, junction nodes and fixed-grade nodes. Junction nodes are

defined as connections of two or more pipes or a location where flow is removed or input

to the system. A fixed-grade node is a node where the values for pressure and elevation are

fixed. Reservoirs, tanks, and large constant pressure mains are classified as fixed-grade

nodes (Mays and Tung, 2002).

Equations used in a hydraulic analysis of a water distribution system are expressed in two

different forms. Loop equations show mass continuity and energy conservation in relation

to the flow in each pipe section. The other form, node equations, expresses mass continuity

in terms of grades at junction nodes. The KYPIPE program utilizes loop equations when

performing hydraulic simulations; it has been shown that loop equation have superior

convergence behavior over node equations (Wood, 1981). Therefore, the loop equations

will be focused on in this section.

The information provided in Equation 1 aids in creating continuity and energy

conservation equations. A continuity equation is created for each junction node in this

system, resulting in j continuity equations. These equations, shown in Equation 2,

represent the conservation of mass at a junction node (Wood, 1981).

eoutin QQQ (2)

where inQ represents the flow into the junction, outQ is the flow out of the node, and eQ

represents the external inflow or demand at the junction node. The conservation of mass

concept requires that the sum of mass flows in all pipes entering a junction must equal the

sum of mass flows leaving the junction (Panguluri et al., 2005). Conservation of energy

equations are also created for each independent loop and pathway between fixed-grade

nodes. If the number of pipes, junctions, and fixed-grade nodes are known, Equation 1 can

be used to determine the number of loops in the system. A loop is an independent closed

path in the system, and these can also be identified by visual inspection for a simple

network. For each loop, the conservation of energy must be true, meaning the energy

16

gained from pumps subtracted from the sum of energy or head losses around the loop must

equal zero (Mays and Tung, 2002). The concept is shown in Equation 3.

0pL Eh (3)

where Lh is the energy loss in each pipe and PE is the added energy from pumps. If no

pumps are present in the loop, the energy equation simply states that the sum of head

losses around the loop equals zero. For a system with f fixed-grade nodes, there will be f-1

energy conservation equations for pathways between two fixed-grade nodes. The energy

conservation equation for paths between fixed-grade nodes is shown in Equation 4 (Mays

and Tung, 2002).

pL EhE (4)

where ΔE represents the difference in total grade between the two fixed-grade nodes. To

select the path between the fixed-grade nodes, any connected path of pipes between the

fixed-grade nodes can be chosen. However, it is important to avoid redundant paths. The

continuity and energy equations together create a set of p (number of pipes) loop equations

that are simultaneous nonlinear algebraic equations. An analysis based on the loop

equations requires a set of equations for the flow in all pipes. Therefore, the loop equations

are expressed in terms of the flow in each pipe (Wood, 1981).

The head losses in the pipes, Lh , represent the sum of losses in the pipe, LPh , along with

minor losses, LMh . The head loss in the pipes is primarily attributed to friction along the

pipe walls and turbulence (Panguluri et al., 2005). The head loss for flow in the pipes is

shown in Equation 5 (Wood, 1981).

n

PLP QKh (5)

where PK represents a loss constant that is a function of pipe length, pipe diameter, and

roughness. Several empirical equations have been developed to represent these losses in a

pipe, with the Darcy-Weisbach, Hazen-Williams, and Manning equations being common

forms. All three of these equations relate head or friction loss to the velocity, length of

pipe, pipe diameter, and pipe roughness (Panguluri et al., 2005). Although KYPIPE has the

17

capabilities to perform calculations using the Darcy-Weisbach equations, the Hazen-

Williams equation is most commonly used and will be outlined. The energy loss for flow in

a pipe based on the Hazen-Williams equation using English units is shown in Equation 6

and Equation 7 (Mays and Tung, 2002).

852.1

87.4852.1

852.173.4QK

DC

LQh PLP (6)

87.4852.1

73.4

DC

LKP (7)

where L is the pipe length (in feet), Q is the flow rate (in cubic feet per second), C is the

Hazen-Williams roughness coefficient, and D is the pipe diameter (in feet). The roughness

coefficient is a function of the pipe material and age of the pipe. Because the total head loss

will be dependent on the flow in the pipe, it is desired to first determine the loss constant,

PK , for each pipe.

The minor loss in a pipe, LMh , takes into account losses at fittings, valves, expansions,

contractions, meters, and other components that may disturb flow in the pipe. Like the

equation for friction head loss in a pipe, the minor losses experienced in the system are

dependent on the flow in a pipe. Therefore, the minor loss coefficient, MK , is first

calculated to quantify minor losses independent of flowrates. These equations are shown in

Equation 8 and Equation 9 (Mays and Tung, 2002).

2QKh MLM (8)

22gA

MKM

(9)

where the M term represents the sum of minor loss coefficients for fittings present in

the pipe section, and A is the cross-sectional area of the pipe in square feet. Pumps in water

distribution systems can be described with a constant power input, pump curve, etc. For

purposes of this study, the pump energy, PE , will be expressed by the simple expressions

shown in Equation 10 (Wood, 1981).

18

Q

ZQPE p )( (10)

where the energy provided by a pump, pE , is a function of the flowrate and a pump

constant, Z. The variable Z is calculated by dividing the useful horsepower of a constant

power pump (multiplied by 550) by the specific weight of water. After developing

equations for the head losses in a pipe and energy added by a pump, the energy equation

shown in Equation 4 can be expressed in terms of loss coefficients and the flowrate in the

pipe. This new equation is shown in Equation 11.

)()( 2852.1 QPQKQKE MP (11)

The continuity equation and energy equations create a set of p simultaneous equations that

are collectively called the loop equations. These equations are expressed in terms of the

unknown flowrates in pipes. Because these equations represent nonlinear algebraic

relationships, a direct solution is not possible (Wood, 1981). An iterative solution must be

utilized to solve these equations for the unknown flowrates, and the algorithm utilized by

KYPIPE is outlined below.

A gradient method is used to deal with the nonlinear flowrate terms in the energy equation.

The right hand side of Equation 11 represents the grade difference across a pipe section

with a given flowrate Q. The grade difference, expressed at an approximate value of the

flowrate iQQ , is shown in Equation 12 (Wood, 2010).

)(2852.1

iimipi QPQKQKH (12)

where iH is the grade difference. The gradient function evaluated at the approximate flow

Qi is shown in Equation 13 (Wood, 2010).

)('2852.1 852.0

iiMiPi QPQKQKG (13)

where iG is the gradient function. The linear theory method, the methodology used in

KYPIPE, is based on a simultaneous solution of basic hydraulic equations for the

distribution system. Because the energy equations are nonlinear, the first step in the linear

19

theory method is to linearize the equations with respect to the approximate flow, iQ , in

each pipe. This step is accomplished by taking the derivative of the variables in the

equation for the grade difference (Equation 12) with respect to the flow and evaluating the

results at iQQ . The following linearized equation shown in Equation 14 is the result

when this relationship is used with the energy equation (Wood, 2010).

)( iiii HQGQG (14)

This equation represents each pipe in the given loop, and it is used to create 1 fl

energy equations that are combined with j continuity equations to create a set of p

simultaneous linear equations that are in terms of the flow in each pipe. An initial value is

estimated ( sft /4 ) for the velocity in each pipe, and the linearized equations are solved

utilizing matrix procedures. The new set of flowrates is used to linearize the equation

again, producing an updated second solution. This procedure is repeated until the change in

flowrates between trials is very small (Wood, 1981). Convergence is expected since all

flowrates are computed simultaneously, and a final solution should occur quickly

compared to other methods. Even for an analysis of a large distribution system,

convergence should occur for a high degree of accuracy after only four to eight trials

(Wood, 2010).

2.2.2 KYPIPE Case Study

A study by Wood and Rayes (1981) compared five different methods for solving for the

unknown flowrates in water distribution systems, and the linear method used in KYPIPE

was included in this study. Hardy Cross developed the original method for solving

systems, which solves the loop equations based on adjusting flowrates to individually

balance each of the energy equations (Wood and Rayes, 1981). The flow adjustment factor

calculated for the pipes in each energy equation is applied to all pipes in each path, and

improved solutions are used for each trial until the correction factor is within a desired

limit. Although it is not as widely used, Hardy Cross also proposed a method to solve the

node equations by adjusting the head at each node to balance the continuity equation.

These methods are known as the Hardy Cross method, or the single path adjustment

method (using loop equations) and single node adjustment method (utilizing node

20

equations). The Hardy Cross method requires an initial set of flowrates that balance the

continuity equation at every node, and convergence is partially dependent on how close the

initial set of flowrates is to the actual solution (Wood, 1981). It was noted that this method

sometimes led to convergence problems. This issue, along with the limitations discussed,

resulted in researchers developing new methods for solving distribution systems.

The simultaneous path adjustment method simultaneously adjusts the flowrate in each path

of pipes for an energy equation. An initial set of flowrates that satisfy continuity equations

is first required. A flow adjustment factor is simultaneously calculated for each path that

satisfies the energy equation without disrupting the continuity constraint. Improved

solutions are then used until the flow adjustment factor is within a desired limit. The

simultaneous node adjustment method was also proposed. This method utilizes a

simultaneous solution of the node equations and requires a linearization of the node

equations in respect to approximate values of the head. These simultaneous linear

equations are solved by first assuming a set of junction node heads. The heads are used to

linearize the node equations, and this is repeated until convergence is satisfactory (Wood

and Rayes, 1981). The last method included in the study by Wood and Rayes (1981) is the

linear method. This method is used in the KYPIPE program, and was described in detail in

Section 2.2.1.

These five algorithms were tested on 30 water distribution systems with less than 100

pipes and 21 systems with over 100 pipes (maximum of 509 pipes). Different situations

were examined for each system that included minor changes in system characteristics; 60

situations were examined for systems with less than 100 pipes, and 31 situations were

analyzed for the systems with over 100 pipes. Initial assumptions for flowrates were

assumed where necessary, and trials for each method were executed until the average

change in flowrates between successive trials was less than 0.5 percent. For the loop

equations, accuracy was measured by the unbalanced heads for the energy equations. The

unbalance in continuity was used to quantify solution accuracy for methods using the node

equations (Wood and Rayes, 1981).

All five methods were compared to exact solutions for the systems under 100 pipes. The

exact solution was found by executing the linear method one additional trial after the

21

relative accuracy of 0.005 was achieved. The flowrates and heads at junction nodes were

compared to the exact solutions for each method in the study.

After observing results from the study, the linear method proved to be very reliable. It

converged for every situation and system, and the number of trials required to reach the

solution averaged at 6.4 trials (for the 31 systems with over 100 pipes). The simultaneous

path method also had good convergence characteristics, and only one failure occurred.

Failure occurs when the relative accuracy is reached but the average percent deviation

based on flowrate and head range from the actual solution exceeded 10 percent and the

maximum percent deviations exceeded 30 percent, or the maximum number of trials is

executed without reaching the desired accuracy. This method requires an average of 8.5

trials to reach the desired accuracy (Wood and Rayes, 1981).

The other three methods tested did show problems with convergence. The frequency of

problems increased with the larger systems, but only the results for the systems less than

100 pipes were presented. Eight failures were observed for the single path adjustment

(Hardy Cross) method. All eight cases reached the specified accuracy, and six had a

relatively low unbalanced head. Five of the eight failures improved with additional trials,

but three failures still occurred. Two of these failures recorded large unbalanced heads, but

one failure seemed to reach a good solution with a high degree of relative accuracy and a

low unbalanced head. However, the flowrates had significant error from the actual results.

Wood and Rayes believe this error occurred because some pipes with significantly high or

low head losses were included in the same energy equations (Wood and Rayes, 1981).

The simultaneous node adjustment method resulted in a total of 18 failures, and the

patterns and characteristics of the failures varied greatly. Some failures had an accurate

calculation of heads, but large errors in the flowrates. Other failures never reached the

desired accuracy regardless of the number of trials executed. The single node adjustment

(Hardy Cross) method was the least reliable of all five algorithms investigated. The

specified accuracy was achieved in many cases, but the solutions had large errors in the

flowrates. A total of 51 failures occurred. When the specified accuracy was changed from

0.005 to 0.0005, a satisfactory solution was found for all but two situations. Even though

22

most solutions did improve, convergence was slow and it was difficult to ensure that the

solution was satisfactory.

This Wood and Rayes study (1981) showed that significant convergence problems

occurred using the single path and node adjustment (Hardy Cross) methods and

simultaneous node adjustment method. The single adjustment methods must be executed to

a greater accuracy to increase the likelihood of finding a satisfactory solution. Strict

convergence requirements for unbalanced head (path method) and unbalanced flow (node

method) may also improve reliability, but this does not guarantee accuracy in the final

results. The study also showed that the simultaneous node method proved to be unreliable.

Results showed that if convergence was not met in a reasonable number of trials (40 in this

particular study), additional trials usually did not improve the results. These three methods

that displayed issues with convergence all require an initial set of flowrates or heads. The

probability of failure can be reduced if initial guesses for flow and head are close to actual

results. However, a good set of initial values still does not guarantee convergence (Wood

and Rayes, 1981).

The simultaneous path and linear methods both proved to have good convergence

characteristics. A relative flow accuracy of 0.005 is adequate for finding accurate flows

and heads. Because gradient methods are used to deal with the nonlinear terms in the

energy equations, convergence issues are always possible. However, convergence

problems are unlikely while using the linear or simultaneous path methods. Although both

methods are desired when solving distribution systems, this study showed that the linear

method is slightly more accurate because it resulted in zero failures while the simultaneous

path method led to one failure (Wood and Rayes, 1981). Because the KYPIPE program

utilizes the linear method to solve for the unknown flowrates in the pipes (and pressure at

the nodes), it can be assumed that the program is reliable for providing an accurate

hydraulic analysis of a system. A reliable hydraulic analysis is the first step in providing an

accurate tool for determining optimal sensor placement.

2.3 Current Trends in Sensor Placement

As previously discussed, the placement of sensors in water distribution systems has

become a critical component of contamination warning systems. Many optimization

23

methods for placement of sensors require an understanding of flow dynamics and general

behavior in the system. This behavior can be estimated by using a simulation-based

analysis utilizing calibrated hydraulic and water quality models of the network. However,

water quality and hydraulic models require significant expertise and calibration to produce

an effective model. Many water utilities, especially utilities serving small populations, do

not have the resources to build effective models of their system. Even if a model exists, the

execution of sensor placement software, along with the computational requirements, can be

problematic. Placing sensors based only on hydraulic models negates the need for

information about contaminant behavior, but still requires the need for a hydraulic model

(Xu et al., 2008). Not only is the development of accurate system models potentially

problematic, but optimization methods used to determine sensor placement also have

limitations.

A number of different optimization methods have been used to address the sensor

placement issue, including integer programming (IP) solvers, genetic algorithms, and local

search. Other accepted heuristic optimization methods have also been utilized. However,

optimization methods can be limited by the performance guarantee for the final solution,

the available computer memory, and the runtime required for performing the optimization.

IP solvers can guarantee the best sensor placement that minimizes contamination risk, but

they often have difficulties solving large applications. Computational runtime and memory

space can be problematic using IP solvers. Heuristic optimization methods, such as genetic

algorithms and local search methods, usually cannot guarantee that the final solution is

optimal. However, these methods can typically find near optimal solutions in a short time

(Hart and Murray, 2010).

It is desired to develop a simple method of sensor placement guidance to aid small utilities

with limited resources in sensor placement. Ideally, sensor placement guidance would

avoid the limitations experienced with optimization methods and accomplish guidance

without the need for water quality modeling, or even hydraulic models of the distribution

system. This section presents various research studies aimed at new methods in optimal

sensor placement.

24

2.3.1 Betweenness Centrality and Receivability

A study by Xu et al. (2008) simplifies the sensor placement problem by applying a graph-

theoretic, or network analysis, approach, which eliminates the need for a calibrated water

quality model. An undirected graph represents the physical structure of a water distribution

network, and it does not require hydraulic information about the system. This helps shed

light on identifying structurally important nodes, which may have implications on the

optimal placement of sensors. A parameter called betweenness centrality is used to define

the centrality of a node in terms of the degree to which the node is located on the shortest

path between other sets of nodes. Nodes with high betweenness centrality lie on the path of

many pairs of other nodes, and these nodes would also be between many potential

upstream contamination events and downstream receptor populations. Therefore, the

authors argue that nodes with high betweenness centrality would be potential locations for

sensors. After observing a set of water distribution networks, it was noted that nodes with

high betweenness centrality tend to cluster in the network. This concept of clustering is

shown in Figure 5.

Figure 5: Clustering of Nodes with High Betweenness Centrality (Xu et al., 2008)

Selecting several sensor locations based solely on this parameter would result in clustered

sensors and redundant information. Therefore, the concept of dividing the system into a set

of exhaustive and mutually exclusive communities, and then selecting the node with the

highest betweenness centrality within each community, was introduced. The sensor

25

locations would also be biased towards the downstream nodes to increase the detection

likelihood. The concept of dividing the system into communities is displayed in Figure 6.

Figure 6: Community Divide in a WDS (Xu et al., 2008)

The general procedure of using this methodology to predict optimal sensor placement is

as follows. An undirected graph is created to describe the system, and an adjacency

matrix (N x N) is created based on the physical structure of the network (N is the number

of nodes in the network). For every node in the system, the graph distance between the

node and each water source is found, and then the shortest distance is chosen. Next, the

number of communities created in the system is set to the number of sensors to be placed.

Within each community, a node with a high betweennness centrality and a long graph

distance from water sources is selected as the potential sensor location. This is shown in

Figure 7.

26

Figure 7: Selected Nodes within each Community (Xu et al., 2008)

Xu et al. (2008) also utilized the concept of receivability, used to describe the set and

number of nodes that have paths to the measured node in a graph. This concept is

developed from reachability. The reachability concept says that if there is one or more

paths from node i to node j, then node j is reachable from node i and node i is receivable

to node j. Receivability is able to measure the capability of a node to detect

contamination events; sensors located at nodes with high receivability should detect more

contamination events. To maximize the detection likelihood, it is desired to maximize the

coverage of the sensors. Therefore, the set of nodes with the highest receivability would

maximize coverage in the system.

The general procedure of selecting a set of nodes based off non-time constrained

receivability is outlined. A dynamic directed graph, which represents the system

including flow direction, is developed for the network (again creating an N x N adjacency

matrix). The receivability of each node is calculated utilizing a breadth-first search

algorithm. The first sensor is placed at the node with the highest receivability, and the

nodes covered by this sensor are then removed. The second sensor is placed at the node

with the highest receivability among the remaining nodes. This process continues until all

desired sensors are placed, or until all nodes in the system are covered. For placing

sensors based on time constrained receivability, the process is similar. The values in the

adjacency matrix are water travel time instead of a binary 1 or 0 based on whether water

27

flows between two nodes during the study period. Sensor placement based on the concept

of receivability is shown in Figure 8.

Figure 8: Sensor Placement based on Receivability (Xu et al., 2008)

Xu et al. (2008) tested their theory by placing sensors in a system based on betweenness

centrality, non-time constrained receivability, time constrained receivability, and the

exhaustive simulation analysis. Sensor placed was measured on detection time,

population at risk, volume of water contaminated, and detection likelihood. Results

showed that the exhaustive simulation based approach performed better than time-

constrained receivability, which also performed better than the betweenness centrality

approach. However, the differences in performance between the methods were not

significant. The comparison between non-time constrained receivability and the

simulation showed very similar results for detection likelihood. Therefore, when a utility

is not able to develop and calibrate a model of their system, they can effectively use

information about the physical structure of the system and the methods presented to

identify key nodes for sensor placement. These methods will be close in effectiveness to

an exhaustive simulation. Xu et al. (2008) also points out that receivability metrics are

useful in educating the utility, formulating a mitigation plan for a contamination event,

and developing preventative measures.

28

2.3.2 Rule-Based Expert System

Another study by Chang et al. (2011) worked to develop a rule-based expert system

(RBES) to generate sensor deployment methods without the computational burden

typically encountered with optimization methods. The system was constructed using a

combination of EPANET and Excel with a goal of addressing the complexity of the

system and reducing the computer runtime while achieving the same level of robustness.

This RBES utilizes the accessibility rule and complexity rule to achieve these goals. Each

rule is used to analyze the system independently, and then the results are used to find a

common set of nodes for final sensor locations.

The accessibility rule utilizes results from a hydraulic simulation performed in EPANET

to determine the flow fraction for nodes in the network. The flow fraction is found with

the flow from the main pipeline, a pipe with a larger diameter at each node, and the flow

in a secondary pipeline, a pipe with a smaller diameter than the main pipe. A higher flow

fraction means that the population density downstream of the node is higher because of

the higher baseline demand in the downstream nodes (Chang et al., 2011). Because flow

in a pipe is driven by the downstream water demand, the flow fraction can also be

assumed as an index used to estimate the percentage of population that could be affected

in the case of an unexpected contamination event (Chang et al., 2012a). The flow fraction

is calculated for every node with at least one or more secondary pipes connected to it.

The accessibility rule is used to rank the nodes from highest to lowest flow fraction in the

system, and the design objective of this rule is to maximize flow fraction. The authors

argue that deploying sensors upstream will prevent the highly populated downstream area

from exposure to the contaminants, and this maximizes the total population being

protected while also meeting budget constraints by using a small number of sensors.

The complexity rule classifies nodes in the distribution system as inner nodes or path

nodes. A path node has one or more pipes connected to the main pipe (junction with three

or more pipes connected to it), and an inner node is located between two path nodes

(maximum of two pipes connected at the junction). Figure 9 illustrates this concept.

29

Figure 9: Inner Nodes and Path Nodes (Chang et al., 2011)

The complexity rule proceeds to determine the number of inner nodes with a hydraulic

connection to the path node systematically (Chang et al., 2011). The complexity rule

works to deconstruct the node structure configuration to account for a larger population

that could possibly be affected by a contamination event, eliminating the need to consider

temporal variability (Chang et al., 2012a). An effective radius for each path node is

calculated by finding the summation of all pipe distances from a path node to each inner

node, and then this value is divided by the number of inner nodes for each path node in a

system. The paths nodes are then ranked from the highest number of inner nodes to the

lowest, and optimal sensor locations are selected as path nodes with the highest number

of inner nodes. In other words, the goal of the complexity rule is to determine the number

of path nodes with the maximum combined number of inner nodes based on the path

nodes impact zone. The impact zone is found by averaging the distance from all inner

nodes with a hydraulic connection to the path node. The path nodes are ranked based on

the number of inner nodes located in the impact zone, where the path nodes with the

highest number of inner nodes are considered as sensor locations. The optimal sensor

location is ideally found by using both the complexity and accessibility rules

simultaneously.

To measure the performance of the RBES, the strategies were implemented on models

used in “The Battle of the Water Sensor Network (BWSN): A Design Challenge for

Engineers and Algorithms’, held as part of the Annual Water Distribution Systems

Analysis (WDSA) in Cincinnati, OH in August 2006. The RBES was compared to the 14

optimization and heuristic models presented at the BWSN for four design objectives. The

30

RBES outperforms over half of the optimization and heuristic models in terms of the time

to detection, population affected, and consumption of contaminant objective. The two

rules developed with RBES, derived to address effectiveness and efficiency required for

sensor deployment, can compete with most of the optimization models (Chang et al.,

2011).

2.3.3 Rule-Based Decision Support System

Chang et al. (2012a) also expanded this concept to a rule-based decision support system

(RBDSS), which utilizes the same complexity and accessibility rules. Each rule follows

the same concepts previously outlined, and each rule is applied independently in

sequence to produce two sets of sensors. Further selection is carried out according to half

of the number of sensors from each independent set. Using an equally weighted

optimization method instead of using both rules simultaneously to generate a single set of

sensor locations will improve the probability of detection. If a system can afford to place

six sensors, the accessibility rule should be used to generate three locations, and the

complexity rule should be implemented to place the remaining three sensors.

The RBDSS expands the node classification concept to derive an effective radius. This

improved complexity rule was developed to adjust for a large-scale network with a large

number of inner nodes, it can also be used to improve analysis of small systems. The

improved complexity rule will cause sensor locations to be closer to highly populated

areas and improve performance with design objectives. To find the effective radius for

each node in the system, the distances from the pipe connecting the node of interest to its

hydraulically connected neighbors in all directions were calculated. The number of nodes

within the effective radius is counted. After finding all combined inner nodes and path

nodes, the nodes are ranked in descending order based on the inner nodes and path nodes

counted (Chang et al., 2012a).

The RBDSS was tested with Network 2 used in the BWSN, and it was compared against

11 models and algorithms for four objectives. The results of RBDSS proved to be

competitive in comparison; it outperformed several other models for all four objectives.

RBDSS is also advantageous because it produces one set of sensor locations that

31

represent an optimal solution and it can be executed with inexpensive software such as

Excel (Chang et al., 2012b).

Further work by Chang et al. (2012b) expanded the rule-based decision support system

(RBDSS) to include the intensity rule, along with the accessibility and complexity rules.

The improved RBDSS aims to generate near-optimal sensor deployment strategies with

low computational burden with the addition of the intensity rule to accompany the two

rules previously discussed. The system was also designed to minimize the total number of

sensors needed and maximize the monitoring coverage in order to increase the cost

effectiveness of a sensor deployment system.

The intensity rule focuses on the concentration of contaminants in the system. Because

the maximum contaminant levels (MCLs) in a network are regulated by the U.S.

Environmental Protection Agency (EPA), the intensity rule was analyzed before the

accessibility or complexity rules. The intensity rule uses information of population

exposure, so EPANET was used to complete the vulnerability assessment. The main goal

of this rule is to ensure that the concentration of potential contaminants such as

microorganisms, disinfection by-products, disinfectants, inorganic chemicals, etc. remain

under MCLs. The intensity rule can be utilized with many chemical species of concern to

prevent public harm relating to accidental or intentional contamination events.

The first-order decay was used in the chlorine residual analysis in EPANET. Nodes are

ranked from highest to lowest based on how much they exceed the MCLs at any point

during the day. Nodes that exceed the MCLs are ranked highest, and the top ranked nodes

are chosen as sensor locations. For chemicals that have to meet minimum concentration

requirements, the goal is to minimize the summation of total concentrations at the nodes

that violate minimum standards (Chang et al., 2012b).

Applying the intensity rule, along with the previously discusses complexity and

accessibility rules, results in near-optimal sensor placement guidance. The general

procedure for execution of RBDSS is shown in Figure 10.

32

Figure 10: General Procedure for RBDSS (Chang et al., 2012a).

The three rules were tested on the Hardin County No. 1 water distribution system in

Elizabethtown, Kentucky. The system is considered a small water distribution network;

the capacity of the plant is 2 million gallons per day (MGD). The data was first analyzed

by the intensity rule to choose more than 10 possible nodes. These nodes were then

evaluated by the accessibility rule to narrow down the possible sensor nodes, and the

final sensor nodes were chosen after eliminating more possibilities using the complexity

rule.

Based on the intensity rule, the location with the highest population density is selected as

a sensor location more since higher exposure levels occur along the main pipe and tanks.

This was consistent with results of the accessibility and complexity rules, because flow

fractions in these areas should be higher and the number of inner nodes should be picked

up more often. The authors state that RBDSS is an effective tool that eliminates the

impact of changing flow direction in pipes, and it can also be applied to distribution

systems elsewhere with any scale (Chang et al., 2012a).

2.3.4 Demand and Reachability

A study by Isovitsch and VanBriesen (2008) looked at the spatial trends in sensor

placement determined by optimization methods. The relationship between sensor location

and water demand was also analyzed. The authors caution that they believe sensor

placement is most likely dependent on network hydraulics, but the goal of their spatial

33

analysis is to improve understanding of sensor network design criteria that would

hopefully lead to simplification of design methods.

In this study, Geographic Information Systems (GIS) is used to develop a visual

inspection of the frequency of sensor placement and analyze the spatial relationships

among the sensor locations determined in the BWSN. Further analysis was performed in

GIS utilizing the average nearest neighbor and spatial autocorrelation tools. GIS is also

used to aid in a chi-square analysis to investigate dependence of sensor location on node

attributes such as demand or reachability.

The average nearest neighbor (ANN) tool is used to determine the degree of clustering

among nodes by measuring the extent to which the spatial distribution of nodes differs

from a randomly distributed set. An average nearest neighbor ratio (R) was generated.

This value is found by comparing the average distance to the mean nearest neighbor

distance for a random distribution. The value used for average distance is found by

measuring the Euclidean distance (length of a straight line between two nodes which is

not always equal to pipe distance) between each node and its nearest neighboring node

and then averaging these measurements. R values that are closer to zero mean the nodes

are more clustered. The spatial autocorrelation tool aims to measure the underlying

pattern between nodes based on their location. It provides information about how

clustered, random, or dispersed the data are.

In the study, an analysis was performed on a distribution system using four scenarios and

five objectives. Sensor placement was determined using an optimization method that

accounted for time to detection, population affected, contaminated water demand, and

detection likelihood. Results from the average nearest neighbor analysis showed that

sensor locations were clustered (with a less than 1 percent likelihood that the pattern

could be the result of random chance), and the first sensors placed were more intensely

clustered.

The authors hypothesized that average demand may be an effective indicator of optimal

sensor placement because population affected and contaminated water consumed were

design objectives. EPANET was used to find a value for average demand at every node

34

over the 48 hour simulation. The frequency of the networks with 20 sensors placed was

compared with average demands, and no relationships were obvious. The frequency

analysis was not able to make conclusions for a correlation between sensor placement

and average demand. Reachability and reachable average demand was also investigated.

Reachability is the number of nodes in the network to which water can flow from the

node in question. Reachable average demand represents the total demand for all nodes

that are reachable from the node in question. A visual inspection in GIS was used to

investigate the relationship between these parameters and sensor placement. There was

not an obvious relationship present when looking at all cases and scenarios together.

However, when the systems were divided according to objective, some patterns were

observed.

When examining reachability of selected sensor nodes, the optimal nodes had high

reachability for the objectives of expected population affected and contaminated water

demand. When sensor selection was based off expected time to detection and detection

likelihood, the selected sensor nodes had low reachability. Similar results were observed

for average reachable demand. These results make sense because population affected and

contaminated water demand are functions of average demand, so optimal sensor locations

should have high reachable demand and high reachability. When observing results

generated from the detection likelihood objective, sensor locations will be most likely to

detect contaminants, disregarding the time to detect these contaminants. Therefore, this

would result in sensor placement on the exterior of the system at nodes with low

reachability and low average reachable demand.

A chi-square analysis was performed to further investigate the relationship between

sensor placement and average demand, reachable average demand, and reachability. The

chi-square analysis eliminates the effects of overlapping sensor locations. Results of the

analysis showed that more sensor nodes than expected had high average demand,

although this relationship was not strong. The analysis performed on all cases for all

objectives showed no significant dependence of sensor placement on reachability and

reachable average demand. However, some dependencies were observed when each

different objective function was examined independently.

35

A statistically significant dependency was found between sensor placement and high

average demand for the objective functions time to detection and detection likelihood.

When the objectives for population affected and contaminated water demand were used, a

dependency between sensor nodes with high reachability and high reachable demand was

noted. Sensors were more likely to be placed at nodes with low reachability and low

reachable average demand when using the detection likelihood objective. The results of

this study show that using a system attribute like average demand for certain design

objectives, specifically time to detection, could be practical for water utilities (Isovitsch

and VanBriesen, 2008).

Designing sensor placement in a system based solely on one design criteria can be

dangerous. If sensor placement design is executed on a system by emphasizing any one

design criteria over others, various trials using different objectives could result in a

different sensor placement pattern (Aral et al., 2010). The goal of contamination warning

systems is to reduce the exposed population to the contaminant and reduce contaminated

water volume. It makes sense that this goal would be accomplished by minimizing the time

to detection with high reliability (Aral et al., 2010). Therefore, further work in this area

will utilize data provided by sensor placement software that recommends sensor placement

based on low time to detection. Sensor placement determined by this objective function for

numerous contaminant injection scenarios will be used to explore possible trends that

could exist between optimal sensor nodes and system parameters. Parameters previously

studied in the literature, such as demand, accessibility, complexity, etc. will be further

examined. Other network parameters, such as proximity to storage tanks, pipe capacities,

and connectivity to surrounding nodes will also be investigated. Further work in this area

will focus on developing sensor placement guidance based on easily measurable network

characteristics, with the hope that this guidance will be a helpful tool for sensor placement

for small utilities.

36

CHAPTER 3

3 Water Distribution System Models

The main objective of this research is to develop guidance for optimal sensor placement

that would be applicable to small utilities. Sensor placement software is used to evaluate

optimal sensor placement for a database of water distribution system models. This database

consists of 15 models that can be classified as small systems based on the typical service

demand of the system. All the systems used in this research average a daily demand

between one and three million gallons per day (MGD).

The models used in this study were also selected based on their spatial configuration and

diversity in general system characteristics. All 15 models can be characterized as grid,

loop, or branch spatial configuration. The systems also provide variation in their basic

system characteristics such as number and size of pumps, tanks, and reservoirs. The

variation in system components was meant to avoid apparent trends in sensor placement of

models within a configuration that were actually just caused by similarities in system

components. The goal of this study is to develop trends for optimal sensor placement

within system configuration that would be applicable to any small utility classified as the

given configuration. Using distribution system models that have a variety of characteristics

for each configuration will allow the trends to be applicable to a range of systems.

3.1 System Configurations

Each model used in this research can be classified as one of the three basic system

configurations for water distribution networks: branch, loop, or grid. Figure 11 shows a

diagram displaying the basic setup of each system configuration.

37

Figure 11: System Configurations (a) Loop, (b) Grid, (c) Branch (taken Gagliardi and

Liberatore).

A branch system is named for its similarities to a tree branch. Smaller pipes branch off

more centralized, larger pipes so that water can theoretically only take one path from the

source to customers (National Research Council, 2006). This type of system is frequently

used in rural areas where the service area is fairly large, but some consumers in the far

branches are spaced far apart from each other. High flows are experienced in the large

transmission lines running through the center of the system, and lower flows are present in

distribution mains as pipes become smaller farther away from the center of the system.

These systems contained more pumps, tanks, and a greater total length of water lines

because the systems are more spread out. Even though these systems typically contain a

greater total length of pipeline than other configurations, the average diameter of pipes are

usually smaller. An example of a system in branch configuration is shown in Figure 12.

38

Figure 12: System in Branch Configuration

The branch system is typically easy to distinguish, but the loop and grid systems have

similar characteristics and it is sometimes difficult to classify systems into these

configurations. Both systems consist of connected loops of pipelines, allowing several

pathways that the water can flow from the source to customers. These system

configurations are more widely used in large municipal areas or densely populated systems

(U.S. Environmental Protection Agency, 2008).

Loop and grid systems are considered very reliable because line breaks can be easily

isolated, allowing only a small portion of the system to be affected (National Research

Council, 2006). Looping is not only advantageous because it provides continuous service

even if a portion of the system is shut down, but it also provides flow from multiple

directions for reliable fire flow and reduces the number of dead-ends that potentially cause

water quality problems (McGhee, 1991).

39

In loop systems, there is typically a large, centralized transmission line that feeds smaller

lines. The purpose of the central lines is to supply high flows from the source through the

middle of the system, and the system then transitions to lower flows as the lines move

outward from the central area. These smaller lines connect at each end into the main loop

(Gagliardi and Liberatore). An example of a real distribution system classified as a loop

configuration is shown in Figure 13.

Figure 13: System in Loop Configuration

In grid configured systems, the water lines are laid out to look similar to a checkerboard.

The main water line infrastructure, that are typically the larger pipes in the system, loop

around the outside of the network. The system then transitions to smaller pipes in the

interior of the system. Pipe sizes usually decrease as the distance away from the supply

source increases (Gagliardi and Liberatore). An example of a distribution system in grid

configuration is shown in Figure 14.

40

Figure 14: System in Grid Configuration

Many systems are a combination of different configurations (systems containing both

looped and branch configurations are common). However, for the purposes of this

research, all systems were classified strictly as one configuration based on which

configuration characteristics were most prominent.

3.2 General Procedures of Model Development

The model database used in this research consisted of 15 models, all representing real

distribution systems located in Kentucky. 12 models were used with the sensor placement

software, and the remaining three models will be used in the future for verification of the

developed trends. The Kentucky Infrastructure Authority (KIA) sponsored the Water

Resources Information System (WRIS). This system contains shapefiles representing water

41

distribution system components for all utilities established in Kentucky (Kentucky

Infrastructure Authority, 2010). The shapefiles for water lines, pumps, tanks, and water

treatment plants were downloaded and imported to ArcGIS (Geographic Information

System). The files for meters, surface sources, well sources, and purchase sources were

also available, but these components were not necessary for the purpose of this project.

Each shapefile contained data about a system component for the entire state, and the

“Owner” attribute was utilized to isolate the system components for each individual utility

used in the study. Figure 15 displays the entire water line shapefile for Kentucky, along

with the water lines of one utility isolated.

Figure 15: Water Line Shapefile.

The shapefiles acquired from the KIA database do not have elevation data associated with

them, which is necessary to perform a hydraulic analysis in KYPIPE. Therefore, digital

elevation models (DEM) were necessary to assign elevations to system components. This

data was acquired from the National Resources Conservation Service (United States

Department of Agriculture). Once shapefiles containing system components and elevation

data was acquired, a series of imports and exports of data between GIS and KYPIPE was

executed to create a working hydraulic model. The general process of model creation is

outlined in Figure 16. A step-by-step procedure for creation of models in KYPIPE is

outlined in Appendix B.

42

Figure 16: Model Development Procedure.

3.2.1 Pipe Roughness Coefficients

In order to make the system models as representative to the actual distribution system as

possible, roughness values were added to all pipes in the system. This allowed the model to

account for head loss in the pipes due to friction, and the KYPIPE model uses the Hazen-

Williams equation as the basis for head loss. The Hazen-Williams equation is widely used

to relate the physical properties and flow parameters of a pipe to the resulting head loss or

pressure drop that will occur. A widely used version of the equation in English units is

shown in Equation 15 (Mays, 2005).

87.4852.1

852.173.4

DC

QLhL

(15)

where Lh is the head loss (ft), L is length of pipe (ft), Q represents the flow rate (cfs), C is

the Hazen-Williams C-Factor (also known as roughness factor), and D is the diameter of

the pipe (ft). The roughness coefficient, C, used in the Hazen-Williams equation varies for

pipes based on pipe material and age of the pipe. Different pipe materials will result in

varying roughness factors because pipe roughness is dependent on pipe material. Steel and

43

PVC pipes tend to be smoother and result in less friction loss than cast iron pipes (AWWA,

2005).

The roughness coefficient is also dependent on the age of the pipe. New pipes are typically

very smooth and have not yet undergone a great deal of corrosion and deposition, resulting

in minimal head loss. After time, the pipes will accumulate deposits and experience

tuberculation on the interior of the pipe. This reduces the actual inside diameter of the pipe,

causing the actual inside diameter to be less than the expected nominal diameter, which

allows less water than expected to flow through the pipe. The accumulation of deposits

also causes greater frictional head loss from the increased roughness in the pipe (Walski et

al., 2003).

In terms of the roughness coefficient, C, used in the Hazen-Williams equation, the

frictional head loss experienced in the pipe will increase as the coefficient decreases.

Therefore, pipes made out of smoother material, such as PVC, will have higher C

coefficients than materials with greater roughness values like cast iron. Similarly, older

pipes of the same material that have experienced significant corrosion and deposition will

have lower coefficients than new pipes of the same material (AWWA, 2005). If the flow

rate remains constant, a smaller roughness coefficient will result in a larger pressure drop

in a segment of pipe.

Hazen-Williams coefficients were applied to all pipes in the model to estimate head loss

and increase accuracy in the model. A “reference roughness” value based on pipe material

was entered to represent the roughness factor of a new pipe. An “aged roughness (10 yr)”

value was also entered to represent the reduced roughness coefficient of the pipe after 10

years. These values were also used to estimate roughness of pipes that were greater than 10

years old. The procedure for applying these values to pipes is outlined in Appendix B.

3.2.2 Model Demand Input

To create a model that is a close reflection of an actual water distribution system, water

demand data also needed to be incorporated into the model. The most accurate method of

demand distribution would be acquiring meter data from the utility and applying this actual

44

demand data to nodes throughout the system. This process was not feasible for creating a

database of 15 models, so an estimation of the demand allocation was used.

In order to add demand data to the distribution system model, data was acquired for the

total average daily demand in the given system. This data was acquired from the WRIS

database (Kentucky Infrastructure Authority, 2010), and demand data was provided for

total water usage in million gallons per year. This data was converted to million gallons

per day, then to gallons per minute (GPM) to match the units in KYPIPE. The “Automatic

Demand Distribution” tool in KYPIPE was used to allocate the total demand to nodes

throughout the system. This tool distributes the total demand to nodes in the system based

on the diameters of adjacent pipes. It assigns greater values of demand to larger pipes,

modeling higher flows in large pipes and lower flows in small pipes. This is fairly

representative of how a real system operates, except for the case of large transmission

lines. The main purpose of these larger pipes is to transmit water to smaller arterial and

distribution mains, which then deliver water to customers. Transmission lines usually do

not directly service a high amount of demand (Mays, 2000). However, this process does

meet the goal of distributing the total average daily demand throughout the system in the

general pattern that smaller pipes will service lower demands. This demand allocation was

accurate enough for the purposes of this research.

Because water usage in a typical water distribution system has varying water demand

patterns throughout the day, it was necessary to investigate demand patterns over a 24 hour

period. For example, residential areas will typically have higher demand in the early

morning/evening and lower demand during the day when residents are at work. Demand in

areas mainly consisting of businesses and industrial plants will reflect the operating hours

of the facilities, usually during the day between 8 a.m. and 5 p.m.

Although it would be more accurate to develop demand patterns based on water usage

types (residential, commercial, or industrial), this data would be very time consuming to

acquire so implementing a demand pattern that applied to all parts of the system was

sufficient for the purposes of this research. KYPIPE calculated nodal demand by

multiplying the stated demand by a “demand factor” that is defined for every time period

in the hydraulic analysis (1 hour). This caused the hourly demand to change from the

45

average hourly demand in the system, either by increasing or decreasing the demand at a

given hour based on the time of day. The demand factors applied to the models were

developed by the American Water Works Association. The factors were less than one

during the night when demand would be low and above one during the day when demand

is the highest. The highest demand factors were set during the evenings when customers

are cooking dinner and showering before bed. This allowed the model to simulate the

demand spikes that are experienced during the day in a real distribution system. The

procedure for applying demands and demand factors to the models are outlined in

Appendix B.

3.2.3 Final Adjustments to Model

Some of the system characteristics used to describe distribution systems in the model

database were further modified to simulate the behavior of real systems. Changes were

made to create models that operated under reasonable pressure ranges, and this was

determined to be between 40 and 150 psi. Problems with low pressures in systems were

typically solved by raising initial tank levels (raising minimum and maximum tank levels

in some cases), increasing roughness value of pipes, and increasing the power of pumps. In

cases of extreme low pressure, pumps were added to the system. To correct high pressures

experienced in some systems, initial tank levels were lowered (along with minimum and

maximum tank levels in some cases), roughness values for pipes were decreased, the

power of some pumps was decreased, and pressure regulators were added in some areas. In

some cases of extreme high pressure, pumps were removed completely. This is reasonable

because some pumps were necessary in systems to help transmit flows to nearby systems,

but these connecting systems nearby were not included in the distribution system model of

concern.

Control switches were also added to the model to simulate the pump schedules controlled

by the utility. Control switches are able to turn pumps on and off based on the level

(pressure, head, or HGL) of a certain node in the system. In a typical system, pumps are

turned on to fill tanks when they get to a low water level, and this typically occurs at a low

demand time. Control switches were applied to certain pumps in the system that had a

primary purpose of filling tanks. Pumps present in the system to provide pressure usually

46

did not have control switches. These control switches caused the pumps to turn off when

the tanks reached a high water level (usually close to the maximum tank level) and turn

back on when the tank reached a low level (close to the minimum tank level). These

control switches help simulate the real behavior of water distribution systems that is

typically controlled and monitored by the utility. The procedure used to implement these

changes in KYPIPE is outlined in Appendix B.

Ideally, a field calibration would be executed for each water distribution system to create

models that accurately represent the actual system. Elevation data for all system

components would be verified with surveying, and hydraulic field testing would be

executed to determine actual roughness coefficients for the pipes. However, the model

calibration process is time consuming and requires a great deal of labor and data collection.

For the purposes of this research, a full scale calibration is not necessary. The small

changes made to the system to simulate realistic flow conditions are adequate for this

study.

All changes made to the model database were reasonable alterations that were not

unrealistic conditions for small water distribution systems. For example, pipe roughness

coefficients were not changed outside of the reasonable range for a given pipe material.

This ensures results realistic of an actual distribution system, even if data was slightly

altered from that of the actual system. The necessity to make minor changes to the model

occurs for two main reasons. First, some characteristics of systems are determined based

on the presence of connections to nearby systems. Because any connections to neighboring

systems were not included, some aspects were altered to make them suitable for use solely

in the system of concern. Also, some data acquired from the KIA database for certain

systems was outdated or incorrect. In one case, the values for maximum elevation of water

in the tanks were lower than the ground elevation of the tanks. Because this data was

clearly incorrect, steps were taken to develop more realistic estimates for these values.

3.3 Description of Models used in Study

The model database used in this research consists of 15 hydraulic models. However, only

12 models were used in this phase of the research in testing of the sensor placement tool.

The remaining three models will be used in the future for verification of sensor placement

47

guidance. The models represent real distribution systems in Kentucky, but all models were

given a name in the form KY #. All identifying information for the actual systems

represented by the models was removed, such as names of pumps and tanks, to protect the

security of the utilities. Model names were grouped by configuration type. The first four

models, KY 1 – KY 4, along with KY 13 are in the loop configuration. The first four

models (KY 1 – KY 4) were used to develop sensor placement guidance, while the last

model KY 13 will be used to verify the newly developed guidance. The layout of each

system in the loop configuration is displayed in Figure 17. A detailed layout of each

distribution system model is shown in Appendix C. The schematic of each loop system

shows a centralized transmission line running through the center of the system, and smaller

water lines deliver water to the exteriors of the system. These smaller lines form looping

patterns that provide water multiple pathways to reaching most parts of the system.

Figure 17: Systems in Loop Configuration: (A) KY1; (B)KY2; (C) KY3; (D) KY4; (E) KY13

The models KY 5 – KY 8, along with KY 14, are classified as models in grid

configuration. Similar to the loop configuration models, the first four models (KY 5 – KY

48

8) were used in the development of sensor placement trends, while KY 14 will only be

used in the verification process. The general layout of each system classified in the grid

configuration is displayed in Figure 18. The system schematics show main transmission

lines looping around the exterior of the system, along with the same looping patterns on

smaller distribution lines similar to that of loop configured systems.

Figure 18: Systems in Grid Configuration: (A) KY5; (B) KY6; (C) KY8; (D) KY14; (E) KY7

The remaining models, KY 9 – KY 12 and KY 15, can be classified as branch

configuration systems. KY 9 – KY 12 were used with sensor placement software to

develop guidelines for sensor placement, and the final model KY 15 will be used to

verify sensor placement trends in the future. Figure 19 displays the layout of each model

in branch configuration. These systems layouts display how branch systems resemble a

tree in that water lines branch out from the main transmission lines. The branch systems

are more spread out than other systems, and they contain more tanks and pumps as a

49

result of the large area covered. These systems also contain more dead ends as the system

branches out, varying from the looping pattern of pipes in the grid and loop systems.

Figure 19: Systems in Branch Configuration: (A) KY9; (B) KY10; (C) KY11; (D) KY12; (E)

KY15

Even though all system models are classified into three configurations, each system has

varying characteristics that distinguish them from other systems within each

configuration. A detailed layout of each distribution system model, including all system

components, is shown in Appendix C. The data displayed in

Table 1 also shows differences in system characteristics among the systems. The data

shows a variation in characteristics, including number of tanks, number of pumps,

number of reservoirs, total length of water lines, number of nodes, and total system

demand. However, there are still trends present based on system configuration

classification. For example, the branch systems all have a greater number of tanks and

pumps, and they also have greater total length of water lines than the other systems.

50

Table 1: System Characteristics

System

Name

Config-

uration

Numbe

r of

Tanks

Number

of

Pumps

Number of

Reservoirs

Total

Length

of Pipes

(miles)

Number

of Nodes

Total

System

Demand

(MGD)

KY 1 Loop 4 7 3 103.6 796 1.50

KY 2 Loop 3 1 1 94.6 766 2.09

KY 3 Loop 3 5 3 56.7 279 2.02

KY 4 Loop 4 2 1 162.1 949 1.51

KY 5 Grid 3 9 4 60.0 401 2.28

KY 6 Grid 3 2 2 76.5 512 1.56

KY 7 Grid 3 1 1 85.2 478 1.53

KY 8 Grid 5 5 2 153.7 1274 2.47

KY 9 Branch 15 20 4 597.7 1256 1.38

KY 10 Branch 13 13 2 267.2 931 2.26

KY 11 Branch 28 21 1 285.4 829 1.93

KY 12 Branch 7 16 1 403.1 2338 1.38

KY 13 Loop 5 4 2 95.2 733 2.36

KY 14 Grid 3 6 4 64.5 350 1.04

KY 15 Branch 8 13 2 299.5 669 1.52

3.4 Steady State and EPS Simulations

During the model development phase of this research, the models were first run under a

steady state simulation. This feature in KYPIPE runs the simulation at time t=0 and does

not continue the simulation over an extended period of time. The program uses all initial

settings to execute the hydraulic analysis. For example, the initial tank levels are used as

the head in the tanks. Alterations were first made to the models to ensure they ran

successfully, and within the desired pressure range, for the steady state simulation.

An extended period simulation (EPS) was then set up in KYPIPE. The total time of the

simulation, computational period, and report period can be specified in the program. For

all EPS simulations in this study, the total times were set as 24 hours, and both the

computational and report periods were set as one hour. The starting time was set as hour 0.

These parameters will run the simulation for 24 hours and output all hydraulic results

51

every hour during the 24 hours. The control switches, used to regulate the operation of

pumps in the systems based on tank levels, were also added at this point. The control

switches caused pumps to turn on when tank levels were low and turn off when tank levels

reached high levels. The purpose and settings for control switches are discussed in further

detail in Section 3.2.3.

The EPS calculates hydraulic conditions in the system over the entire computational period

at the specified computational periods. The program displays all data provided in the

steady state simulation (pressure, HGL, head, demand at all nodes and flow, velocity, and

loss at all pipes) at every report period. Hydraulic conditions are also provided at times

when a tank is emptied or filled. The KYPIPE program displays data at all nodes and pipes

in both tabular and graphical form. The program also provides data for the

pressure/HGL/head/flow at all tanks, reservoirs, and pumps in the system.

52

CHAPTER 4

4 KYPIPE Tool Sensor Placement Analysis

4.1 Theory

The Water Quality sensor placement tool has been developed to work with the existing

KYPIPE graphical user interface. The goal is to provide a simple tool to aid utility

managers in the optimal placement of sensors within their distribution systems. The tool

will recommend optimal locations for online sensors based on simple water quality

analyses and methods that require very little or no added input from utilities. The

simplicity and ease of use of the sensor placement tool makes it attractive for the use in

small utilities. Its enumeration methods used to determine optimal sensor placement also

ensure accurate and useful results.

The sensor placement tool recommends optimal sensor placement, regardless of how many

sensors are implemented, based on minimizing time to detection. The tool considers

detection events at nodes throughout the entire system, and recommends optimal sensor

placement based on the locations that can detect contamination events the fastest. The

enumeration process executed by the sensor placement tool is explained in detail.

The tool first reads the INP file (hydraulic network model data file in EPANET format)

and makes the necessary changes in order to perform a simple water quality analysis of

conservative constituents. The tool then performs water quality simulations, where the

injection of a contaminant is dependent on the user defined input. The WQ sensor

placement tool is able to recommend optimal sensor placement for up to five sensors, but

the explanation on theory will use placement of two sensors. The tool will first consider

the first possible combination of two sensors. Next, the first possible injection site in the

system will be selected. The contaminant is “injected” at the injection site, and the travel

time for the contaminant to reach each of the sensors is determined. Because the

contaminant is considered to be detected when it reaches the first sensor, the minimum of

the two travel times is taken. This is used as the travel time for this particular set of

possible sensor nodes and injection site (it is not necessary for the contaminant to be

detected at both sensors). Detection is based on the detection limit entered in the default

parameters option. For this study, a detection limit of 0.01 mg/l was used. When the

53

concentration of the contaminant reaches 0.01 mg/l at the particular sensor node, the

contaminant is considered to be detected. The tool considers 24 hours as the maximum

travel time. Any travel time past 24 hours will be considered 24 hours for calculation

purposes.

This theory is illustrated Figure 20. The values for T1 and T2 represent the travel times

from the injection node to sensor 1 and sensor 2, respectively. The travel time assigned to

this particular set of sensor locations and injection location will be 360 minutes, because it

is the minimum of the two travel times. The minimum travel time is now considered the

travel time from that injection site for the set of two sensor locations.

Figure 20: Sensor Placement Tool Theory (Minimum Travel Time)

This process is repeated for all possible injection nodes in the system. The minimum of

the two travel times for all possible injection nodes is taken for the same set of possible

sensors. The average travel time for the particular set of sensor locations is calculated by

averaging the minimum travel times from all injection sites. The sum of travel times from

all injection sites is calculated and divided by the total number of injection nodes to

determine the average travel time for that set of sensor locations. This concept is

illustrated in Figure 21.

54

Figure 21: Sensor Placement Tool Theory (Average Travel Time)

This process is then repeated for every possible set of sensor locations (in this case, every

possible set of two sensors), resulting in an average travel time for every possible

combination of two sensors in the system. The sensor combination with the lowest average

travel time will be considered the optimal sensor location.

When selecting nodes for potential sensor locations, certain requirements are needed such

as accessibility, security, and protection from the environment. Obvious locations that

satisfy all requirements are utility owned locations such pumping stations, tanks, valve

stations, etc. However, other locations could be easily adapted to meet requirements, such

as fire/police stations, schools, city buildings, etc. Even customer connections could be

adapted to meet sensor location needs, although securing access to private homes or

businesses could be problematic. However, a longer list of feasible sensor sites results in a

sensor design that is more likely to perform well. So the benefits of using sites that need

some adaptation to meet requirements may be worth the additional costs (Murray et al.,

2008). Because considering many nodes for potential sensor locations is ideal, the KYPIPE

tool considers possible sensor locations to be all nodes (including tanks, pumps, reservoirs,

and junctions) except dead-end nodes. The average travel time to dead-end nodes will

generally be much higher, skewing the average times to detection. Possible injection sites

are considered to be all non-zero demand nodes, excluding dead-end nodes. Dead-end

nodes are considered to be consumption nodes, so any contaminant injected at these nodes

will be consumed immediately and the contaminant will not be able to travel further in the

55

system. The reality of this concept may be slightly different, but this assumption is used in

the sensor placement tool.

4.2 Procedure for Execution

In order to obtain optimal sensor placement in a water distribution system using the sensor

placement tool, a model of the system must first be developed in KYPIPE. The model

should accurately depict the structure of the system, and it should include characteristics of

all system components such as tanks, pumps, reservoirs, and pipes. The process of model

development is outlined in Section 3.2.

Once the system runs effectively for a steady state simulation, an extended period

simulation (EPS) is set up for the model. For this study, the total time was set to 24 hours,

and both the computational period and report period were set to one hour. The EPS needs

to be executed on the model. After the analysis is complete, the sensor placement tool is

started by using the shortcut Shift + F7. The sensor placement tool open will window, and

parameters are entered, starting with the number of sensors to place. Under default

parameters, the total simulation time, WQ computational time, mass injection rate,

injection start time, injection end time, and detection limit values are also entered. The INP

file is generated, and then the sensor placement tool is run. When the run is completed, the

tool will output the name of the optimal sensor locations, along with the average travel

time for the chosen sensors. The general procedure for execution of the sensor placement

tool is outlined in Figure 22. A detailed step-by-step procedure for using the sensor

placement tool, along with all parameters used in this study, is outlined in Appendix D. A

poster that can be used as a tool to aid utilities in executing the sensor placement tool is

included in Appendix A.

56

Figure 22: Sensor Placement Tool Flowchart.

When the sensor placement tool is executed, an Excel file with the file name

systemnameTimeMatrix.csv will be generated. The file includes all data used to determine

the sensor location with the lowest time to detection. The first column shows all possible

sensor nodes, and the first row of nodes represents the injection nodes. The values show

the travel times between the injection and sensor nodes (in minutes). If the cell shows 0 for

a travel time, this means that the sensor nodes are too far away from the injection nodes

and the contaminant will not reach the sensor within 24 hours. Therefore, the travel time is

considered to be 24 hours for calculation purposes. A report of the simulation (text

document) can also be accessed. The report provides information about parameters used in

the simulation, along with the selected sensors and average travel time in hours. This file,

along with the time matrix, can be accessed in the systemname.KYP folder.

4.3 Performance Evaluation

4.3.1 Contamination Scenarios

The sensor placement tool was executed on the 12 models in the model database for 15

different contamination scenarios. The contamination scenario is determined by both the

rate of injection of the contaminant (in mg/min) and the total injection time (in hours).

57

Contamination scenarios were created for three different general scenarios: fixed amount,

fixed rate, and fixed time. Each general scenario is comprised of five specific sets of an

injection rate with a total injection time. The theory behind the three different

contamination scenarios is explained.

For the fixed amount scenarios, the scenario simulates a drum of contaminant to be

injected, and it is desired to inject the entire drum. The pump speed used to inject the

contaminant can be varied and unlimited time is available. The fixed rate scenarios

simulate an injection pump with a constant speed, so injection rate cannot be varied.

However, unlimited time and materials (contaminant) is available. The fixed time

scenarios model a limited amount of time available to inject the contaminants, but the

pump speed can be varied and supplies are unlimited. The 15 contamination scenarios

performed on each model in the model database are displayed in Table 2.

Table 2: Contamination Scenarios

Injection Rate

(mg/min)

Injection

Time (hours)

Total Contaminant

Injected (mg)

Fixed

Amount

(Vary

Time)

4000 1 4000

2000 2 4000

1000 4 4000

500 8 4000

250 16 4000

Fixed

Rate

(Vary

Amount)

1000 1 1000

1000 2 2000

1000 4 4000

1000 8 8000

1000 16 16000

Fixed

Time

(Vary

Rate)

600 4 2400

800 4 3200

1000 4 4000

1200 4 4800

1400 4 5600

All 15 contamination scenarios were executed on all water distribution system models

using both TEVA-SPOT and KYPIPE. It was desired to compare the sensor placement

58

results, both sensor placement and times to detection, between KYPIPE and TEVA-SPOT

for a variety of scenarios. To be able to directly compare results from the two sensor

placement programs, it was ensured that all parameters matched between the programs.

First, the models used in each were identical. The TEVA-SPOT program uses a model

input from EPANET. Even though minor differences exist between KYPIPE and

EPANET, all major system components and characteristics of these components matched

between the two programs. This included pipe roughness values, grade of reservoirs,

pump power, pipe diameters, etc. An example of a difference between KYPIPE and

EPANET is that KYPIPE allows tanks to be measured as a total volume or fixed diameter,

while EPANET only allows a fixed diameter as input for tank size. To make the models as

similar as possible, all tanks in both KYPIPE and EPANET were set as fixed diameters.

Parameters used in the sensor placement tool in KYPIPE and TEVA-SPOT were also

standardized. The WQ computational time (labeled as hydraulic timestep in TEVA-SPOT)

were both set to 60 seconds (or one minute in TEVA-SPOT), and the total simulation time

was set to 24 hours. The detection limit for both programs was also set to 0.01 mg/l. This

ensured one program would not detect the contaminant faster than the other simply

because it had a lower detection limit. A study investigating the impact of sensor detection

limit on performance showed that a sensor detection limit of 0.01 of the average source

concentration was adequate for maximum protection for the example system examined

(McKenna et al., 2006).

4.3.2 Time to Detection Comparison

The baseline contamination scenario (a contaminant injected at 1000 mg/min for four

hours) was considered the baseline case because it was present in all three general

contamination scenarios. The comparisons between the two programs for the baselines

conditions for all models are shown in this section, and the results for all remaining

contamination scenarios are included in Appendix E. Table 3 displays the sensor

placement simulation results for both KYPIPE and TEVA-SPOT for the baseline

contamination scenario. The table displays the time to detection calculated by both

programs (KYPIPE outputs time in hours and these were converted to minutes). The

column labeled “Fastest Time to Detection” shows which program resulted in the lowest

59

time to detection for that particular system. The last column displays the difference

between the higher and lower detection times (in minutes).

Table 3: Comparison between KYPIPE and TEVA-SPOT for Baseline Conditions

System

Time to Detection (min) Fastest Time

to Detection

Difference (Higher

Time - Lower Time)

in min KYPIPE TEVA-

SPOT

1 sensor

KY1 930.0 955.45 KYPIPE 25.45

KY 2 793.2 789.61 TEVA-SPOT 3.59

KY 3 784.2 815.89 KYPIPE 31.68

KY 4 765.0 787.41 KYPIPE 22.41

KY 5 557.4 553.65 TEVA-SPOT 3.75

KY 6 663.0 696.69 KYPIPE 33.68

KY 7 916.8 894.50 TEVA-SPOT 22.30

KY 8 1033.8 985.16 TEVA-SPOT 48.65

KY 9 1359.6 1370.09 KYPIPE 10.49

KY 10 988.8 995.02 KYPIPE 6.22

KY 11 1257.6 1302.11 KYPIPE 44.51

KY 12 1224.0 1236.31 KYPIPE 12.31

2 sensor

KY1 807.0 834.47 KYPIPE 27.47

KY 2 486.6 555.89 KYPIPE 69.29

KY 3 531.6 575.02 KYPIPE 43.42

KY 4 711.0 730.27 KYPIPE 19.27

KY 5 487.8 478.27 TEVA-SPOT 9.53

KY 6 574.8 602.12 KYPIPE 27.32

KY 7 697.2 585.00 TEVA-SPOT 112.20

KY 8 877.2 870.33 TEVA-SPOT 6.87

KY 9 1296.0 1310.83 KYPIPE 14.83

KY 10 855.0 899.43 KYPIPE 44.43

KY 11 1180.2 1227.15 KYPIPE 46.95

KY 12 1171.2 1185.17 KYPIPE 13.97

The results in Table 3 show that the sensors selected by KYPIPE led to lower times to

detection for most, but not all, system models. For the one sensor scenario, KYPIPE had

lower times to detection for eight out of the 12 models. When placing two sensors,

KYPIPE produced lower times to detection for nine out of the 12 models. TEVA-SPOT

60

calculated lower times to detection for KY 5, KY 7, and KY 8 for both one and two

sensors. These results can also be seen in Figure 23 and Figure 24. Figure 23 displays the

time to detection comparison for one sensor placement, and Figure 24 shows the same

comparisons for placement of two sensors.

Figure 23: Comparison between KYPIPE and TEVA-SPOT - Baseline Conditions (1 sensor)

0

200

400

600

800

1000

1200

1400

KY1 KY 2 KY 3 KY 4 KY 5 KY 6 KY 7 KY 8 KY 9 KY 10 KY 11 KY 12

Tim

e to

Det

ecti

on

(m

in)

System

Baseline Conditions (1000 mg/min x 4 hr) - 1 sensor

TEVA-SPOT

KYPIPE

61

Figure 24: Comparison between KYPIPE and TEVA-SPOT - Baseline Conditions (2 sensors)

The results were also investigated to determine which program resulted in faster times to

detection for all 15 contamination scenarios performed on each system. The data showing

the number (out of a total of 15 scenarios) and percentage of contamination scenarios that

led to lower times to detection using each program is displayed in Table 4.

0

200

400

600

800

1000

1200

1400


Tim

e to

Det

ecti

on

(m

in)

System

Baseline Conditions (1000 mg/min x 4 hr) - 2 sensors

TEVA-SPOT

KYPIPE

62

Table 4: Analysis of Faster Times to Detection between KYPIPE and TEVA-SPOT

System

TEVA-SPOT KYPIPE

Number of

scenarios that

resulted in

lower time to

detection

Percentage

of scenarios

that resulted

in lower time

to detection

Number of

scenarios that

resulted in

lower time to

detection

Percentage of

scenarios that

resulted in

lower time to

detection

1 sensor

KY 1 0 0% 15 100%

KY 2 9 60% 6 40%

KY 3 0 0% 15 100%

KY 4 0 0% 15 100%

KY 5 12 80% 3 20%

KY 6 0 0% 15 100%

KY 7 15 100% 0 0%

KY 8 15 100% 0 0%

KY 9 0 0% 15 100%

KY 10 0 0% 15 100%

KY 11 0 0% 15 100%

KY 12 0 0% 15 100%

2 sensors

KY 1 0 0% 15 100%

KY 2 0 0% 15 100%

KY 3 0 0% 15 100%

KY 4 0 0% 15 100%

KY 5 15 100% 0 0%

KY 6 0 0% 15 100%

KY 7 15 100% 0 0%

KY 8 13 87% 2 13%

KY 9 0 0% 15 100%

KY 10 0 0% 15 100%

KY 11 0 0% 15 100%

KY 12 0 0% 15 100%

Examining the results shown in Table 4, TEVA-SPOT had lower times to detection for the

majority of contamination scenarios for only four out of the 12 systems, when placing one

sensor. KYPIPE produced faster times to detection for the majority of scenarios for the

remaining eight systems for the placement of one sensor. When placing two sensors,

TEVA-SPOT had faster times for the majority of contamination scenarios for three

systems, and KYPIPE produced faster times for the majority of scenarios in nine systems.

This analysis reflects the same results presented for the baseline conditions in Table 3.

63

These results outlined above prove the effectiveness of the KYPIPE sensor placement tool.

For the majority of system models, just observing results from the baseline contamination

scenario, KYPIPE selected sensors that had lower times to detection. This is effective in

accomplishing the goal of online quality monitoring, to assess water quality and alert

operators of a contamination event. Faster detection times of a contamination event will

result in quicker notification to those affected and fewer negative effects.

The KYPIPE sensor placement tool also has the advantage of its simplicity and ease of

use. After running a successful extended period simulation, the sensor placement tool is

easily activated by using the Shift + F7 keys. Basic parameters are entered into the tool

(number of sensors, total simulation time, WQ computational time, injection rate and time,

injection start and end time, and detection limit). The tool will then run with a fairly short

computation time. The optimal sensor locations are output along with detection time, and

the sensors can be easily displayed on the system map. This is advantageous to utility

managers, and the simplicity and ease of use of the sensor placement tool will allow it to

be an effective tool for utilities.

4.3.3 Comparison of Identical Sensor Placement

Along with comparing the times to detection from KYPIPE and TEVA-SPOT, the sensor

locations chosen as optimal sensors by both programs were also compared. Some

contamination scenarios for the same system model resulted in TEVA-SPOT and KYPIPE

selecting the same sensor nodes as the optimal locations. Other scenarios lead to different

locations chosen as the optimal sensor locations between KYPIPE and TEVA-SPOT. Even

in cases where different sensors were chosen by the programs, the times to detection for

the chosen sensors were typically very similar. For each system model, the selected sensors

for all 15 contamination scenarios were investigated. The number of contamination

scenarios that resulted in identical sensor selection between KYPIPE and TEVA-SPOT,

out of the 15 total scenarios, was recorded. This data (for placement of one sensor) is

shown in Table 5. The percentage of contamination scenarios resulting in identical sensor

selection between KYPIPE and TEVA-SPOT is also illustrated in Figure 25.

64

Table 5: Identical Sensor Selection between KYPIPE and TEVA-SPOT (1 sensor)

System

Scenarios with

Matching

Sensor Selection

Percentage of Scenarios

with Matching Sensor

Selection

KY1 13 86.7%

KY 2 5 33.3%

KY 3 15 100.0%

KY 4 14 93.3%

KY 5 2 13.3%

KY 6 15 100.0%

KY 7 12 80.0%

KY 8 0 0.0%

KY 9 0 0.0%

KY 10 15 100.0%

KY 11 0 0.0%

KY 12 1 6.7%

Figure 25: Identical Sensor Selection between KYPIPE and TEVA-SPOT (1 sensor)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Pe

rce

nta

ge w

ith

Ide

nti

cal S

en

sor

System

Percentage of Contamination Scenarios with Identical Sensor Selection (KYPIPE and TEVA-SPOT) - 1 sensor

65

After observing the data in Table 5 and Figure 25, it is clear that some system models had

matching sensor selection between KYPIPE and TEVA-SPOT for all 15 contamination

scenarios, while other systems did not have any matching sensor placement among

contamination scenarios. Three of the 12 systems had matching optimal sensor nodes for

all 15 scenarios, and six of the 12 models had at least 80 percent matching sensor nodes

for the placement of one sensor. On average, 7.7 out of the 15 scenarios (51 percent)

resulted in identical placement of sensors. There were three systems that did not have any

identical sensor nodes between KYPIPE and TEVA-SPOT. Even in these systems with

no matching sensors, further investigation revealed that the vast majority of these sensors

were still in close proximity to each other. Only two contamination scenarios (out of the

15 scenarios performed for 12 systems for a total of 180 simulations) led to sensor

locations that were considered to be far away from each other in the distribution system.

Both of these cases were in the KY 7 system.

Figure 26 displays an example of different optimal sensor nodes between KYPIPE and

TEVA-SPOT in KY 2. For three out of the 15 contamination scenarios, TEVA-SPOT

selected J-138 and KYPIPE recommended J-485 as the optimal sensor location (both are

shown in the KYPIPE program for comparison purposes). However, these sensors are in

very close proximity. The sensor placement recommendations between KYPIPE and

TEVA-SPOT are similar.

Figure 26: Example of Differing Sensor Placement in Close Proximity

66

Figure 27 shows an example of selected sensor nodes with KYPIPE and TEVA-SPOT

that do vary considerably in location. For two out of 15 scenarios performed on KY 7,

TEVA-SPOT recommended J-249 and KYPIPE selected J-271. These sensors are not

close in proximity to each other. Even though the times to detection may be similar, a few

cases did lead to recommended sensors between the two programs that were not close to

each other. However, the scenario displayed in Figure 27 was rare, even in the two sensor

placement scenarios.

Figure 27: Example of Differing Sensor Placement not in Close Proximity

The location of selected sensors between KYPIPE and TEVA-SPOT was also

investigated for the placement of two sensors. For the same 15 contamination scenarios

performed on all 12 models, the number of scenarios with one out of two matching

sensors between KYPIPE and TEVA-SPOT was found. The number of scenarios that

resulted in two out of two identical sensors between the two programs was also recorded.

This data is shown in Table 6 and Figure 28.

67

Table 6: Identical Sensor Selection between KYPIPE and TEVA-SPOT (2 sensors)

System

Scenarios with 1

(out of 2)

Matching Sensors

Percentage of

Scenarios with 1

(out of 2)

Matching Sensors

Scenarios

with 2

Matching

Sensors

Percentage of

Scenarios with 2

Matching

Sensors

KY1 0 0.0% 11 73.3%

KY 2 5 33.3% 8 53.3%

KY 3 0 0.0% 15 100.0%

KY 4 1 6.7% 13 86.7%

KY 5 4 26.7% 0 0.0%

KY 6 3 20.0% 12 80.0%

KY 7 2 13.3% 0 0.0%

KY 8 4 26.7% 1 6.7%

KY 9 1 6.7% 0 0.0%

KY 10 15 100.0% 0 0.0%

KY 11 0 0.0% 0 0.0%

KY 12 6 40.0% 6 40.0%

Figure 28: Identical Sensor Selection between KYPIPE and TEVA-SPOT (2 sensors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Pe

rce

nta

ge w

ith

Ide

nti

cal S

en

sors

System

Percentage of Contamination Scenarios with Identical Sensors Selection (KYPIPE and TEVA-SPOT) - 2 sensors

1 of 2 sensors

2 of 2 sensors

68

The data presented in Table 6 and Figure 28 shows similar trends in identical sensor

placement as with the one sensor scenario. Some of the system models resulted in both

sensors matching between KYPIPE and TEVA-SPOT for many of the 15 contamination

scenario, while other systems did not have any identical sensors placed for any of the 15

scenarios. KY 3 was the only system to have identical sensor placement for both sensors

between KYPIPE and TEVA-SPOT for all 15 contamination scenarios, and KY 11 was the

only system without any matching sensors between the two programs. All other systems

ranged in the percentage of scenarios that matched one out of two or two out of two

sensors. KY 1, KY 2, KY 3, KY 4, and KY 6 all resulted in two out of two identical

selected sensors for over 50 percent of the contamination scenarios, showing that KYPIPE

and TEVA-SPOT produced very similar results in these systems. The cases where

KYPIPE and TEVA-SPOT recommended different sensor nodes were investigated. As

with the placement of only one sensor, the vast majority of these cases resulted in

placement of sensors that were in close proximity to each other. Only a few cases produced

results where the sensors recommended by KYPIPE and TEVA-SPOT were significantly

far away from each other. Specifically, 14 cases (out of the 15 simulations run on all 12

systems) led to differing sensor selection between the two programs that varied

considerably spatially. In all of these cases, only one of the two sensors placed showed

significant spatial variation between the two programs.

4.3.4 Results for All Contamination Scenarios

All 12 system models were run for all 15 contamination scenarios using both KYPIPE and

TEVA-SPOT. For each contamination scenario, the program that resulted in the lowest

time to detection was identified. The difference between the times to detection between the

two programs was also calculated. These results for the system KY 1, for the placement of

one sensor, are shown in Table 7.

69

Table 7: Sensor Placement Results for KY 1 (1 sensor)

TEVA-SPOT KYPIPE

Sys-

tem

Injection

Rate

(mg/min)

Injection

Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detection

(hr)

Time to

Detection

(min)

Fastest

Time to

Detection

Difference

in min

(Higher -

Lower

Time)

KY

1

Fixed

Amount

4000 1 J-406 939.84 J-406 15.39 923.4 KYPIPE 16.4

2000 2 J-406 944.26 J-406 15.45 927.0 KYPIPE 17.3

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

500 8 J-406 971.31 J-737 15.83 949.8 KYPIPE 21.5

250 16 J-406 988.53 J-406 16.14 968.4 KYPIPE 20.1

Fixed

Rate

1000 1 J-406 956.80 J-406 15.66 939.6 KYPIPE 17.2

1000 2 J-406 956.56 J-737 15.65 939.0 KYPIPE 17.6

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1000 8 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1000 16 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

Fixed

Time

600 4 J-406 968.61 J-406 15.74 944.4 KYPIPE 24.2

800 4 J-406 961.84 J-406 15.53 931.8 KYPIPE 30.0

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1200 4 J-406 952.13 J-406 15.49 929.4 KYPIPE 22.7

1400 4 J-406 948.20 J-406 15.48 928.8 KYPIPE 19.4

The results for the placement of two sensors in KY 1 are shown in Table 8. The sensor

placement results for all 12 models using both KYPIPE and TEVA-SPOT are included in

Appendix E.

70

Table 8: Sensor Placement Results for KY 1 (2 sensors)

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differen

ce in min

(Higher -

Lower

Time)

KY

1

Fixed

Amount

4000 1 J-235 J-497 817.25 J-235 J-497 13.31 798.6 KYPIPE 18.7

2000 2 J-235 J-497 822.17 J-235 J-497 13.38 802.8 KYPIPE 19.4

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

500 8 J-245 J-406 843.81 J-245 J-406 13.68 820.8 KYPIPE 23.0

250 16 J-244 J-406 852.30 J-244 J-406 13.78 826.8 KYPIPE 25.5

Fixed

Rate

1000 1 J-245 J-406 838.65 J-235 J-497 13.63 817.8 KYPIPE 20.8

1000 2 J-245 J-406 838.40 J-235 J-497 13.49 809.4 KYPIPE 29.0

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1000 8 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1000 16 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

Fixed Time

600 4 J-245 J-406 843.20 J-235 J-497 13.59 815.4 KYPIPE 27.8

800 4 J-244 J-406 839.39 J-235 J-497 13.48 808.8 KYPIPE 30.6

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1200 4 J-235 J-497 825.74 J-235 J-497 13.43 805.8 KYPIPE 19.9

1400 4 J-235 J-497 824.14 J-235 J-497 13.41 804.6 KYPIPE 19.5

71

CHAPTER 5

5 Conclusion

5.1 KYPIPE Sensor Placement Tool Conclusion

TEVA-SPOT has been developed to analyze the vulnerability of drinking water

distribution networks and recommend locations for the deployment of water quality

sensors as a component of a contamination warning system. However, the software is not

appropriate for small utilities in terms of the simplicity and ease of use. The water quality

sensor placement tool was developed with KYPIPE as a simple tool to aid utility managers

in the optimal placement of sensors within their distribution systems. The new sensor

placement tool has been developed to work with the existing KYPIPE graphical user

interface.

The sensor placement tool recommends optimal sensor placement based on minimizing

time to detection. The tool considers detection events at nodes throughout the entire

system, and recommends optimal sensor placement based on the locations that can detect

contamination events the fastest. The WQ sensor placement tool is able to provide sensor

placement for up to five sensors. The sensor placement tool recommends optimal sensor

placement based on an enumeration process. The process considers injection of the

contaminant at every possible injection node for every plausible combination of sensor

locations. The travel time is calculated from each injection node to every possible sensor

node, resulting in an average travel time for each set of possible sensor locations. The

optimal sensor locations are chosen as the nodes with the fastest time to detection,

resulting in a sensor design that can quickly notify the utility of a contamination event. The

development of the sensor placement tool accomplishes the objective of providing a simple

tool to aid small utilities in sensor placement.

To obtain optimal sensor placement in a drinking water system using the sensor placement

tool, a model of the system must first be developed in KYPIPE. The model should

accurately depict the structure of the system and should include characteristics of all

system components. An EPS is set up and executed for the model. To execute the sensor

placement tool, the number of sensors to place, total simulation time, WQ computational

time, mass injection rate, injection start time, injection end time, and detection limit values

72

must be entered. When the run is completed, the tool will output the optimal sensor

locations, along with the average travel time for the selected sensors.

5.2 Comparison of Results between KYPIPE and TEVA-SPOT Conclusion

The KYPIPE sensor placement tool was executed on the 12 distribution system models in

the model database for 15 contamination scenarios. The contamination scenario was

defined by both the rate of injection of the contaminant (in mg/min) and the total injection

time (in hours). Contamination scenarios were created for three different general scenarios:

fixed amount, fixed rate, and fixed time. All 15 contamination scenarios were executed on

all system models using both TEVA-SPOT and KYPIPE (for one and two sensors). The

goal was to compare the sensor placement results, both sensor placement and times to

detection, between KYPIPE and TEVA-SPOT.

To be able to compare results from the two programs, it was important that all parameters

matched between the programs. This included the models used in each along with the

contamination scenarios, possible injection nodes and sensor nodes, etc. The WQ

computational times were both set to 60 seconds, the total simulation time was set to 24

hours, and the detection limit for both programs was also set to 0.01 mg/l.

Examining only the baseline contamination scenario (1000 mg/min for 4 hours) for each

system produced results that were closely reflective of examining all 15 contamination

scenarios. When placing one sensor, KYPIPE resulted in lower times to detection for eight

of the 12 distribution systems. TEVA-SPOT led to faster times to detection for the

remaining four systems. For the placement of two water quality sensors, KYPIPE

produced faster times for nine systems, and TEVA-SPOT led to faster times for three of

the 12 systems. It was also noted that the times to detection between TEVA-SPOT and

KYPIPE for the same system were similar; there were no dramatic differences in times

between the two programs.

The sensor locations chosen as optimal sensor nodes by both programs were also

compared. Some contamination scenarios for the same model resulted in selection of the

same sensor nodes using KYPIPE and TEVA-SPOT, while other scenarios lead to

different locations chosen as the optimal sensor nodes between the programs. For the

73

placement of one sensor, three of the 12 systems had matching sensor locations for all 15

scenarios, and six of the 12 models had at least 80 percent matching sensor nodes for the

placement of one sensor. On average, 7.7 out of the 15 scenarios (51 percent) resulted in

identical placement of sensors between the two programs. There were three systems that

had zero identical sensor nodes between KYPIPE and TEVA-SPOT, but further

examination showed that the vast majority of the different sensors selected by the two

programs were still in close proximity to each other. Only two contamination scenarios

(out of the 15 scenarios performed for 12 systems) resulted in optimal sensor locations that

were considered to be far away from each other in the network.

The optimal sensor locations between KYPIPE and TEVA-SPOT were also investigated

for the placement of two sensors. The data showed similar trends in identical sensor

placement found with the one sensor placement scenarios. KY 3 was the only network to

have identical sensor placement for both sensors between KYPIPE and TEVA-SPOT for

all 15 contamination scenarios, and KY 11 was the only system without any matching

sensors between the programs. KY 1, KY 2, KY 3, KY 4, and KY 6 all resulted in two out

of two identical selected sensors for over 50 percent of the contamination scenarios.

Therefore, the data showed that KYPIPE and TEVA-SPOT produced fairly similar results

in these systems. When KYPIPE and TEVA-SPOT selected different sensor nodes, the

majority of these cases resulted in sensor nodes that were in close proximity to each other.

Only 14 cases (out of the 15 scenarios run on all 12 systems) led to results where the

sensors chosen by KYPIPE and TEVA-SPOT were significantly far away from each other.

In all of these cases, only one of the two sensors placed had significant spatial variation.

The results accomplished the objective of proving the effectiveness of the KYPIPE sensor

placement tool. For the majority of system models, KYPIPE selected sensors that had

lower times to detection. This accomplishes the goal of online quality monitoring to assess

water quality and alert operators of a contamination event. The KYPIPE sensor placement

tool is also beneficial because it is simple and easy to use. Basic parameters are entered

into the tool, and it will then run with a short computational time. The optimal sensor

locations are output and displayed on the map along with detection times. The simplicity

and ease of use of the sensor placement tool will allow it to be an effective tool for utilities.

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This results acquired with this research provides the foundation for future work on

developing sensor placement guidance. The recommended sensor locations from the

KYPIPE sensor placement tool can be examined to determine if patterns exist based on

system characteristics. The differences in sensor location based on the variation in each

system or general network configuration can also be analyzed. If trends in the placement of

sensor nodes are observed, guidance can be developed to assist small utilities in placing

water quality sensors. Development of sensor placement guidance, without the need for a

costly calibrated hydraulic model, would be greatly beneficial to a small utility in

protecting their water supply.

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CHAPTER 6

6 Acknowledgements

Funding for this research was provided by the U. S. Department of Homeland Security,

Science & Technology Directorate, through a technology development and deployment

program managed by The National Institute for Hometown Security, under Other

Transactions Agreement (OTA) #HSHQDC-07-3-00005. We are very grateful for these

contributions.

In addition, we would like to acknowledge the assistance of Dr. Srini Lingireddy of

KYPIPE, LLC.

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CHAPTER 7

7 References

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AWWA. (2005). Computer Modeling of Water Distribution Systems. Manual of Water Supply

Practices- M32. Denver: American Water Works Association.

Chang, N.-B., Pongsanone, N. P., & Ernest, A. (2011). Comparisons between a rule-based expert

system and optimization models for sensor deployment in a small drinking water

network. Expert Systems with Applications, 10685-10695.

Chang, N.-B., Pongsanone, N. P., & Ernest, A. (2012a). A rule-based decision support system

for sensor deployment in small drinking water systems. Journal of Cleaner Production,

28-37.

Chang, N.-B., Prapinpongsanone, N., & Ernest, A. (2012b). Optimal sensor deployment in a

large-scale complex drinking water network: Comparisons between a rule-based decision

support system and optimization models. Computers and Chemical Engineering, 191-

199.

Gagliardi, M. C., & Liberatore, L. J. (n.d.). Water Systems Piping. Lyndhurst, NJ.

Hart, W. E., & Murray, R. (2010). Review of Sensor Placement Strategies for Contamination

Warning Systems in Drinking Water Distribution Systems. Journal of Water Resources

Planning and Management, 611-619.

Isovitsch, S. L., & VanBriesen, J. M. (2008). Sensor Placement and Optimization Criteria

Dependencies in a Water Distribution System. Journal of Water Resources Planning and

Management, 186-196.

Janke, R., Murray, R., Uber, J., & Taxon, T. (2006). Comparison of Physical Sampling and Real-

Time Monitoring Strategies for Designing a Contamination Warning System in a

Drinking Water Distribution System. Journal of Water Resources Planning and

Management, 310-313.

Kentucky Infrastructure Authority. (2010, February 25). Water Resources Information System.

Retrieved 2012, from http://kia.ky.gov/wris/data.htm

Mays, L. W. (2000). Water Distribution Systems Handbook. New York, NY: McGraw-Hill.

Mays, L. W. (2005). Water Resources Engineering. John Wiley & Sons, Inc.

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Mays, L. W., & Tung, Y.-K. (2002). Hydrosystems Engineering and Management. Highlands

Ranch, CO: Water Resources Publications, LLC.

McGhee, T. J. (1991). Water Supply and Sewage. Hightstown, NJ: McGraw-Hill, Inc.

McKenna, S. A., Hart, D. B., & Yarrington, L. (2006). Impact of Sensor Detection Limits on

Protecting Water Distribution Systems from Contamination Events. Journal of Water

Resources Planning and Management, 305-309.

Murray, R., Hart, W., & Berry, J. (2006). Sensor Network Design for Contamination Warning

Systems: Tools and Applications. American Water Works Association (p. 20).

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Murray, R., Haxton, T., & Janke, R. (2010). Sensor Network Design for Drinking Water

Contamination Warning Systems. Cincinnati, OH: National Homeland Security Research

Center, Office of Research and Development, U.S. Environmental Protection Agency.

Murray, R., Janke, R., & Uber, J. (2004). The Threat Ensemble Vulnerability Assessment

(TEVA) Program for Drinking Water Distribution System Security. Critical Transitions

in Water and Environmental Resources Management (p. 8). Salt Lake City, UT: World

Water Congress.

Murray, R., Janke, R., Hart, W. E., Berry, J. W., Taxon, T., & Uber, J. (2008). Sensor network

design of contamination warning systems: A decision framework. American Water Works

Association, 97-109.

National Research Council. (2006). Drinking Water Distribution Systems: Assessing and

Reducing Risks. Washington, DC: The National Academic Press.

Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., et al. (2008).

The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers

and Algorithms. Journal of Water Resources Planning and Management, 556-568.

Panguluri, S., Grayman, W. M., & Clark, R. M. (2005). Water Distribution System Analysis:

Field Studies, Modeling and Management. Cincinnati, OH: United States Environmental

Protection Agency.

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Systems - A Reference Guide for Operators. Cincinnati, OH: Office of Research and

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Walski, T. M., Chase, D. V., Savic, D. A., Grayman, W., Beckwith, S., & Koelle, E. (2003).

Advanced Water Distribution Modeling and Management. Bentley Institute Press.

Wood, D. J. (1981). Algorithms for Pipe Network Analysis and Their Reliability. Lexington, KY:

University of Kentucky Water Resources Research Institute.

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Wood, D. J., & Rayes, A. (1981). Reliability of Algorithms for Pipe Network Analysis. Journal

of the Hydraulics Division, 1145-1161.

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of Key Nodes for Placing Sensors in Dynamic Water Distribution Networks. Journal of

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79

Appendix A

Tools for Water Utilities

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Visual tools were developed to aid utility managers in understanding their distribution

system and deciding on optimal placement of water quality sensors. The posters shown in

this section were intended to be placed on the wall of a utility office or some other

location where utility workers can quickly and easily reference the information presented.

The poster depicted in Figure 29 will aid a utility in determining which classification of

general system configuration (loop, grid, or branch) represents their water distribution

system. The poster includes a figure at the top portion of the poster defining the general

configuration of each classification based on pipe geometry and size. The lists of bullet

points further define the varying characteristics of each configuration. The three small

figures on the right-hand side of the poster represent three real distribution systems that

represent the different system configurations.

Figure 30 shows a poster to help aid a utility in executing the KYPIPE sensor placement

tool. The flowchart described the general steps involved in the procedure. Above (and

below) each step in the flowchart, a screenshot of the KYPIPE interface is included.

These screenshots will further simplify the process of executing the KYPIPE sensor

placement tool.

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Figure 29: Water Utility Poster (Determining Water Distribution System Configuration)

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Figure 30: Water Utility Poster (Procedure for Executing KYPIPE Sensor Placement Tool)

83

Appendix B

Model Development Procedure

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B.1 Data Acquisition in GIS

The model database used in this research was created by following a procedure that utilized the

Geographic Information Systems (GIS) software. The data for all distribution system

components was first acquired from the Water Resources Information System (Kentucky

Infrastructure Authority, 2010). The “Geospatial Data” link was selected from the WRIS

homepage, displaying zip files containing shapefiles for all systems components for the state of

Kentucky. The shapefiles for water lines, pumps, tanks, and water treatment plants were

downloaded and unzipped (all to the same folder). The files for meters, surface sources, well

sources, and purchase sources were also available, but these components were not necessary for

the purpose of this project. A blank ArcGIS document was opened, and the “Add Data” bottom

was used to add shapefiles for water lines, tanks, pumps, and water treatment plants. The data for

the entire state of Kentucky is shown in Figure 31.

Figure 31: Distribution System Component Shapefiles in GIS

In the process of model creation, only the data for the specific utility of concern is necessary.

The data for one utility was isolated by right clicking on the layer name for each component in

the Table of Contents and selecting “Open Attribute Table.” The “Table Options” icon in the top

left-hand corner was selected and then the “Select by Attributes” option. A clause was then

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formulated to isolate parts of the shapefile based on an attribute. The “Owner” attribute was

double-clicked, followed by the equal sign and the “Get Unique Values” button. The desired

utility name was double clicked to complete the clause, and the “Apply” bottom was clicked.

This action selected all the shapes associated with that utility from the data for the entire state,

and this process is displayed in Figure 32.

Figure 32: Select by Attributes

The shapes for the utility were selected (and shown as highlighted in the table). Next, the

attribute table was closed, the component layer was right-clicked, and the “Create Layer from

Selected Features” option was selected under the “Selection” menu. This created a new layer for

the water lines, pumps, tanks, and water treatment plants for the system of interest. Next, it was

advantageous to calculate the lengths of the water lines. The new water lines layer was right

clicked, and the “Open Attribute Table” was again selected. Under the “Table Options” icon, the

“Add Field” option was chosen. A name was specified for the new field (LENGTH_FT), and

“Double” precision under type was chosen. To calculate the length of the pipes with the new

field, the gray box containing the name of the new field was right-clicked, and “Calculate

Geometry” was selected. The option “Length” was selected under Property, the coordinate

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system was left as the default option, and “Feet US [ft]” was selected as the unit. This process

calculated the length of all water lines, and the steps are displayed in Figure 33.

Figure 33: Calculating Length of Water Lines

Next, the shapefiles of all system components were exported by right-clicking on each layer,

clicking on “Data”, and then selecting “Export Data.” The folder where data was stored was

found, and the data was saved as “Shapefile” under the “Save as Type” option. It was important

to give the file a name that did not exceed eight characters. This process is shown in Figure 34.

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Figure 34: Data Export in GIS

This step was followed for the other system components, making sure to save files for all

components in the same folder. This portion of the model creation process completes the first

phase of data manipulation in GIS.

B.2 Data Input to KYPIPE

Next, the data exported from GIS needed to be input to KYPIPE. To start this process, a new

KYPIPE file was opened. Under the File menu, the “Pipe2000 Utilities” option was selected,

followed by “Import ArcView File.” When the Shape File Import Utility opened, the “Select

shape file folder” box was selected, and the pipes shapefile that was exported from GIS was

found and opened. The utility showed a list of fields in KYPIPE on the left side of the screen

under the drop-down menu, and the right portion of the screen displayed attributes present in the

shapefile from GIS. Characteristics of each system component were able to be transferred from

the attribute table in GIS to the KYPIPE file. Attributes were matched by clicking on an attribute

on the left list, clicking on the corresponding attribute on the right list, and then selecting the

“Match Selection” box. Once all the desired attributes were matched, the “Fix Connectivity

Errors” and “Check for Crisscross Lines” boxes were checked, and then the “Read Pipe Shape

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File” box was clicked followed by the “Process Pipe Data” box. This procedure is displayed in

Figure 35.

Figure 35: Matching Attributes between GIS and KYPIPE (Pipes)

After the pipe data was processed, the other system components were processed by selecting the

component (by the name given when the layer was exported from GIS) from the drop-down box

on the left of the utility and selecting the bullet for the corresponding component in KYPIPE

from the list on the far-left side of the utility. Attributes were matched for each component,

similar to the process for pipes, and the “Process Data” button was clicked to process each

component. The process for importing the data for pumps is shown in Figure 36.

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Figure 36: Matching Attributes between GIS and KYPIPE (Pumps)

Once all system components were added, the “Save P2K File” button was selected to save the

file. Although it is possible to match numerous attributes between the GIS shapefile and

corresponding KYPIPE data, experience executing this procedure has shown that matching

numerous attributes can lead to error in the model development process. For example, the

shapefiles from GIS for the pumps contain data about capacity and horsepower that would be

useful data in KYPIPE. However, attempts to match these attributes often lead to errors that

resulted in all pumps being absent from the KYPIPE model. It is recommended to only match a

few attributes for each component to be able to distinguish them, and then match all other

necessary attributes by hand. Table 9 shows the recommended attributes to match between GIS

and KYPIE for each system component. The GIS shapefile for Water Treatment Plants (WTP)

corresponds to reservoirs in the KYPIPE Program.

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Table 9: Attribute Matching between GIS and KYPIPE

Component KYPIPE Attribute

(left side)

GIS Attribute

(right side)

Pipes

Diameter SIZE

MaterialRating MATERIAL

Length LENGTH_FT

Reference Year YEARCON

Pumps Name WP_ID

Tanks Name WT_ID

Reservoirs (WTP) Name WTPNAME

B.3 Addition of Elevation Data

Completion of the procedure thus far resulted in a KYPIPE model containing all of the necessary

system components (pipes, tanks, pumps, and reservoirs). All components had accurate (X,Y)

coordinates and system components were connected to each other (the check for this assumption

is discussed later). However, the current model did not have any elevation data associated with

system components. Elevation data is necessary to carry out a hydraulic analysis in the KYPIPE

program. Therefore, the next steps in model development involve acquiring elevation data for the

area encompassing the utility and assigning values of elevation to components in the model.

A Digital Elevation Model (DEM) was acquired from the National Resources Conservation

Service Geospatial Data Gateway (United States Department of Agriculture). After navigating to

the website, the green circle labeled “Get Data” was selected, followed by the desired state and

county. Once the selected county was submitted, the Elevation data was found from the list of

data available to download. The site presented a list of National Elevation Datasets, and it listed

options for 30 meter, 10 meter, or 3 meter datasets depending on the location. These values

indicated the grid cells (a 10 meter DEM will consist of 10m x 10m grid cells with a

corresponding value for elevation), so the smaller grid cells result in more accurate elevation

data. The smallest available grid cell dataset was selected, and the FTP delivery method was

selected (delivery by email). The “Place Order” button was selected to order the desired dataset.

The DEM selection process is shown in Figure 37.

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Figure 37: NRCS Geospatial Data Gateway

Next, a shapefile of all nodes in the KYPIPE model was generated to add to GIS and later

combine with the DEM. The newly created KYPIPE model was opened, and the “Export

ArcView File” was selected in the Pipe2000 Utilities menu. When the utility opened, the

“Nodes” bullet was selected in the top left-hand corner of the utility. The box for “Name” in the

window below was checked, the right arrow icon was clicked, and then the “Generate Shape

Files” box was selected. This created a shapefile named “Nodes” in the same location as the

.KYP folder, and this process is shown in Figure 38.

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Figure 38: KYPIPE Nodes Shapefile Export

The new nodes shapefile was then added to the ArcMap document (located in the KYPIPE

folder), and the nodes shapefile lined up with the water lines layer. This step of the procedure is

displayed in Figure 39.

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Figure 39: Nodes Shapefile in GIS

The next step of the model creation process was adding the DEM to the GIS file. The DEM files

were downloaded from the email sent by NRCS Geospatial Data Gateway and unzipped. There

were numerous files for the specified county, so the elevation files were added until all of the

nodes in the system were covered by the DEM. In the example shown, three of the 19 DEM

raster provided were needed to cover the area of the distribution system. The newly added DEMs

are shown with the nodes and water line shapefiles in Figure 40.

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Figure 40: Digital Elevation Model

Since more than one DEM raster files was required to cover the entire area of the distribution

system, it was necessary to combine all DEM files. This was accomplished by opening

ArcToolbox, then selecting “Data Management Tools”, “Raster”, “Raster Dataset” and “Mosaic

to New Raster.” When the “Mosaic to New Raster” window appeared, each DEM was selected

from the drop-down menu, a folder location was selected for the new combined DEM, a name

for the DEM was chosen, and the number of bands was set as 1. In order to define the spatial

reference of the DEM, the icon next to the “Spatial Reference for Raster” box was clicked. In the

Spatial reference window, the “Import” box was selected, and the water line shapefile for the

system was found and added as the spatial reference. This step combined all of the necessary

DEM rasters into one combined raster file, and this process is displayed in Figure 41.

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Figure 41: Combined DEM Process

Once the individual DEMs were combined into one, the next step was to extract the elevation

data to each node. First, the “Spatial Analyst” box was checked under the “Customize” then

“Extensions” menu. ArcToolbox was then opened, and the “Spatial Analyst” option was

selected, followed by “Extraction” and “Extract Values to Points.” In the “Extract Value to

Points” tool, the Nodes feature was first selected from the drop-down menu as the input point

feature. The combined DEM was chosen as the input raster, and a location and name was entered

for the new point features. The checkbox labeled “Interpolate values at point locations” was also

checked to interpolate elevations in the DEM data. The process created a new shapefile

containing the nodes from KYPIPE with assigned elevations from the DEM, and the steps are

illustrated in Figure 42.

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Figure 42: Elevation Extraction Process

The previous step created a new shapefile containing elevation data for each node in the system.

The elevations needed to be added to the KYPIPE model, and Microsoft Excel was utilized in

this process. A blank Excel document was opened, the “Open” icon was selected, and the folder

where the shapefile was saved in the previous step was located. On the drop-down menu labeled

“Files of Type”, the option for “All Files” was chosen. The file of nodes with assigned elevations

(created in the previous step) with the file extension “.dbf” was opened. This step is shown in

Figure 43.

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Figure 43: Opening Elevation File in Excel

The Excel file showed the node names in the first column, and the elevation of the node was

contained in the second column. These elevations were in meters, so the elevations were

converted to feet by entering the equation “=B2*3.2808” into the C2 grid. This equation was

applied to all elevation by double clicking on the lower right-hand corner of the C2 cell. Once all

nodal elevations were calculated (in feet), the data needed to be copied to the KYPIPE model.

The model was opened in the KYPIPE program, and the data tables were accessed by selecting

“Data Tables” in the “Edit” menu. The “Nodes” box was clicked in the upper left-hand side of

the screen to access data for all nodes in the system. The column for elevation (labeled “Elv.”)

was located. It was then necessary to sort the data in Excel so the order of node names matched

the order in KYPIPE; this would allow a simple copy and paste of the entire column to elevation

data. To sort the data in Excel, the cells for node name and elevation were highlighted, and the

“Sort” icon was selected under the “Data” menu. The option “NAME” was selected in the “Sort

by” drop-down box, “Values” was entered in the “Sort on” box, and “A to Z” was chosen in the

“Order” box. This process changed the order of node names in Excel to match the order already

present in the Node table of the KYPIPE model, and this step is shown in Figure 44.

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Figure 44: Elevation Data Sorting in Excel

To transfer values of elevation from the Excel document to the KYPIPE model, the entire

column for elevations in feet (starting at cell C2) was highlighted. The column was copied by

hitting “Ctrl + C” and then copied into KYPIPE by hitting “Edit” and “Paste” after the first cell

in the Elevation column was selected in KYPIPE. This step assigned an elevation value to every

node in the KYPIPE model, and the results of this step are displayed in Figure 45.

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Figure 45: Nodal Elevation Data in Excel

B.4 Final Adjustments to Model

Completion of the outlined steps resulted in a model containing all distribution system

components with elevations assigned to each component along with nodes throughout the

system. However, other alterations were required to fix errors that could have occurred in the

model creation process. First, data for tanks including the size, minimum water level, maximum

level, and initial level were entered manually for each tank. The horsepower of each pump was

also entered manually into the model, along with grade of the reservoirs and any other necessary

data.

Various tools in KYPIPE were also utilized to help detect possible errors. Under the “Analyze”

menu, the “Connectivity Check” option was selected (followed by clicking on any pipe in the

system). This tool highlighted pipes that were not connected to the rest of the system. To fix

pipes that were disconnected from the system, the pipe was manually extended in the same

direction to a node in a nearby pipe. If a node was not present nearby, an intermediate node was

added to the nearby pipe and the elevation of this node was interpolated using elevations for the

closest nodes. In all cases, it was very obvious where the pipe should extend and connect to the

system. It was also true in all cases that the pipe appeared to be connected when observing the

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system at a normal zoom level, and the disconnection was only noticeable if the portion was

zoomed in at high levels. Figure 46 illustrates this concept; the left portion represents a normal

zoom level where the disconnection is not noticeable. The problem is clearly noticeable om the

right portion of the figure, but this is a very high level of zoom that the user would not typically

use.

Figure 46: Pipe Connection Errors

Another tool was utilized in KYPIPE to check for other general errors, such as undefined initial

elevations in tanks, an undefined grade in a reservoir, or an extremely high value for pump

power. The tool was utilized by selecting “Error Check” in the “Analyze” menu.

Roughness coefficients were also applied to all pipes in the model to estimate head loss through

the pipes due to friction, and the concept behind these roughness values are discussed in Section

3.2.1. These values were applied to the model by navigating to the “Setups/Defaults” tab on the

main screen, and then selecting the “Pipe Type” tab. The chart displayed each pipe material

present in the model along with all pipe diameters for each material present in the model. Values

were entered for each pipe material and diameter (although roughness coefficients did not vary

based on pipe diameter) to represent the roughness factor of a new pipe in the column labeled

“Reference Roughness.” Values were also entered to represent the reduced roughness coefficient

of the pipe after 10 years in the “Aged Roughness (10 yr)” column. These values were also used

to estimate roughness of pipes that were greater than 10 years old.

To apply these roughness values to pipes in the model, the “Select All Pipes” option was selected

under the “Edit” menu. In the “Edit Pipe Set” box in the upper right-hand corner, “Roughness”

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was selected as the “Item to Edit”, and “Not Fixed” was chosen as the “Operation.” The

“Proceed” button was clicked to apply the set roughness values to pipes in the system, and this

procedure is shown in Figure 47.

Figure 47: Changing Roughness Values of Pipes in KYPIPE

The values used for roughness coefficients in the KYPIPE model are shown in Table 10.

Table 10: Hazen-Williams Roughness Coefficients

Material Reference Roughness Aged Roughness (10 yr)

Cast Iron 130 110

Concrete 125 120

Ductile Iron 130 120

PVC 140 135

PE (Polyethylene) 140 130

AC (asbestos cement) 125 120

To create a model that was a close reflection of an actual water distribution system, water

demand data also needed to be incorporated into the model. This included allocating the total

daily demand to nodes throughout the model and also implementing demand factors to account

for varying water use patterns throughout the day. To find the total daily demand of the system,

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the system in question was found in the WRIS Portal (Kentucky Infrastructure Authority, 2010).

Under the “Planning” tab, the total water usage was given in million gallons per year. This value

was converted to million gallons per day (MGD), and then gallons per minute (GPM) to match

the units used in KYPIPE. In KYPIPE, the “Automatic Demand Distribution” option was

selected under the “Analyze” menu. In the box labeled “Total Demand to Distribute”, the total

daily demand in GPM for the system was entered. The box labeled “Apply this Demand at

Junction Nodes” was clicked, and this applied the total demand to nodes throughout the system

based on pipe diameter. This process is displayed in Figure 48.

Figure 48: Demand Allocation in KYPIPE

Because water usage in a typical water distribution system has varying water demand patterns

throughout the day, it was also necessary to implement demand patterns in the model. In

KYPIPE, the “Demand Patterns” tab was located in the “Setups/Defaults” tab. The “Load”

option was clicked, and the “AWWA.dmt” file was selected and the “OK” button was clicked.

This loaded demand patterns established by the American Water Works Association over a 24

hour period. The demand factors were automatically loaded into the row labeled “Type 1.”

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Because the junctions in the model were automatically set to the “R” (residential) type, it was

necessary to cut and paste the set of demand factors from the “Type 1” row to the “Residential”

row. This ensured the demand factors would be applied to the junctions in the system. This could

also be executed by changing all junctions in the system from “Dm Type R” to “Dm Type 1.”

This process applied demand patterns to the system by changing average hourly demand

throughout the day based on time of day and estimated water use. This process is shown in

Figure 49.

Figure 49: Demand Patterns in KYPIPE

Control switches were also added to the model to help simulate the pump schedule usually

controlled by the utility. KYPIPE uses control switches to turn pumps on and off based on the

level (pressure, head, or HGL) of a certain node in the system. Control switches were applied to

certain pumps in the system that had a primary purpose of filling tanks. These control switches

caused the pumps to turn off when the tanks reached a high water level (usually close to the

maximum tank level) and turn back on when the tank reached a low level (close to the minimum

tank level). In creating the model database, the hydraulic grade line (HGL) was used as the

measurement in control switches. The pump was typically turned on when the tank reached a few

feet above the low level, and it was turned off when the tank was a few feet below the maximum

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level. The control levels were altered based on each specific system. If the tank in question

reached its maximum or minimum tank level (causing extreme high and low pressure,

respectively), the level at which the tank was turned on or off was moved further from the

minimum and maximum tank levels. An example of a control switch setup is shown in Figure

50. This option is reached in KYPIPE by selecting “Control Switches” in the “Other Data” tab.

Figure 50: Control Switches in KYPIPE

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Appendix C

Layout of Database Models

106

The following figures display the layout of each distribution system in the model database. The

figures display all major system components, including tanks, pumps, reservoirs, water lines, and

nodes. The water lines are also displayed using different colors based on pipe diameter.

Figure 51: KY 1 System Layout

107



108



109



110



111



112



113



114

Appendix D

Procedure for Executing Sensor Placement Tool

115

D.1 Execution of Tool

The following section outlines a detailed, step-by-step procedure for executing the sensor

placement tool on a system model in KYPIPE. After the water distribution system model has

been created in KYPIPE (procedure outlined in Appendix B), an Extended Period Simulation

was set up in KYPIPE. This menu is found in the program by selecting the “System Data” tab,

followed by the “EPS (Extended Period Simulation)” tab. In the Extended Period Simulation

menu, the box next to “Use EPS” was first checked. Then values were entered for Total Time

(hrs), Computational Periods (hrs), Report Period (hrs), Default Power Cost ($/kwhr), Starting

Time (hrs 0-24), and Report Time Style. For this study, the total time was set to 24 hours, the

computational and report period was set to 1 hour, the default power cost was left as 0, the

starting time was set to 0 hours, and the report time style was set to ‘Military Time”. This step is

shown in Figure 66.

Figure 66: EPS Setup in KYPIPE

Next, an analysis was run on the distribution system model. This can be executed by clicking the

“Analyze” icon at the bottom right-hand corner of the screen or selecting “Analysis” in the

“Analyze” menu. The execution process by selecting “Analysis” in the “Analyze” drop-down

window is shown in Figure 67.

116

Figure 67: Execution of EPS Analysis

After the hydraulic analysis was completed, the sensor placement tool was executed. The tool

can be initiated by using the shortcut Shift + F7. A window appeared verifying that the sensor

placement tool was starting, and the “OK” icon was clicked.

117

Figure 68: Starting Sensor Placement Tool

The sensor placement tool window appeared, and the number of sensors to be placed was input

to the box in the upper left-hand corner labeled “Number of sensors (max 5)’. In this study,

optimal sensor locations were determined for only one and two sensors. The tool is capable of

placing up to five sensors in a system. After the number of sensors was determined, the box

labeled “Set Default Parameters” located under the input box for number of sensors in the upper

left-hand corner was selected. This menu allowed the user to set values for the total simulation

time (hours), WQ computational time (sec), mass injection rate (mg/min), injection start time

(hours), injection end time (hours), and detection limit (mg/l). The total simulation time was set

to 24 hours, the same time period as the extended period simulation. The WQ computational

time was set for 60 seconds because this matched the computational time set in EPANET and

used in TEVA-SPOT. It was important to ensure all parameters matched between KYPIPE and

TEVA-SPOT for the purposes of this study. However, the user may specify any time period

desired for these values.

118

The mass injection rate refers to the rate at which the “contaminant” is being injected in the

system. For this study, 15 different injection scenarios were executed, and the values for mass

injection rate varied from 250 mg/min to 4000 mg/min. The injection start time and injection end

time determine the length of time, and when that time period occurs in the total simulation time.

Although time duration of injection varied in this study, the injection start time was always set to

0 hours.

The detection limit refers to the concentration of the contaminant that must be present at the

sensor node in order for the sensor to detect the contaminant. The sensor will not “detect” the

contaminant until the specified concentration is present. This study used a detection limit of 0.01

mg/l, which was consistent with the value used in TEVA-SPOT. The process of setting

parameters for the sensor placement tool is displayed in Figure 69.

Figure 69: Setting Parameters in Sensor Placement Tool

After all default parameters were set, the box labeled “Generate INP File” was clicked. After a

few seconds, the text in this box turned from Black to Gray, indicating the INP file was

119

generated. To begin the sensor placement simulation, the “Run” icon was clicked, located below

the “Generate INP File” icon. This process is shown in Figure 70.

Figure 70: Initiating Sensor Placement Run

D.2 Sensor Placement Tool in Progress

As the run was executing, the sensor placement tool displayed the progress of the simulation.

The box in the lower left-hand corner of the tool window displays useful information about the

system, such as the number of nodes, pipes, dead-end nodes, demand nodes, possible injection

nodes, and possible sensor nodes. The tool considers possible sensor locations to be all nodes

(including tanks, pumps, reservoirs, and junctions) except dead-end nodes. The average travel

time to dead-end nodes will generally be much higher, skewing the average times to detection.

Possible injection sites are considered to be all non-zero demand nodes, excluding dead-end

nodes. Dead-end nodes are considered to be consumption nodes, so any contaminant injected at

these nodes will be consumed immediately and the contaminant will not be able to travel further

in the system. The reality of this concept may be slightly different, but this assumption was used

120

in the sensor placement tool. The bottom left-hand corner of the tool window also displays the

default parameters previously entered. Figure 71 displays the sensor placement tool in progress.

Figure 71: Sensor Placement Tool in Progress

Figure 72 displays the sensor placement tool once it has started the sensor placement portion of

the simulation. The box in the lower right-hand corner of the tool, labeled “Optimal Sensor

Locations” displayed the best sensor locations at that point in the simulation. The average travel

time of the current optimal solution was also displayed. The optimal sensor locations updated as

the simulation continued, and this output (along with the average travel time) changed at least

several times before the simulation was complete.

121

Figure 72: Sensor Selection Process

When the sensor placement tool finished running through all possible sensor locations in the

system, it selected the sensor locations with the lowest average travel time as the optimal sensor

locations. When the simulation was complete, the window displayed in Figure 73 appeared. To

continue, the user clicked the icon labeled “OK”.

Figure 73: Completion of Sensor Placement Simulation

122

Once the “OK” icon was clicked, the KYPIPE map reappeared. In the “Optimal Sensor

Locations” box in the lower right-hand corner of the tool, the selected sensors were displayed,

along with the average travel time in hours. The output from the sensor placement tool is

displayed in Figure 74 and Figure 75.

Figure 74: Completed Sensor Placement Simulation

123

Figure 75: Completed Sensor Placement Tool Display

D.3 Results Provided by Tool

In order to view the selected sensors on the map, the node labels were turned on. This was

accomplished by navigating to the “Map Setting” tab and then the “Labels” tab. In the “Node

Labels” box, the “Name” box was checked, along with the “Selected Labels Only” box. This

highlighted the selected nodes in red on the map and created a label with the node name. This

process is shown in Figure 76, and the result is displayed in Figure 77.

124

Figure 76: Turning on Node Labels

Figure 77: Displaying Selected Nodes on the Map

KYPIPE also has several features to observe more detailed results of the sensor placement

simulation. In the lower right-hand corner of the sensor placement tool window, the “View

Report” icon was clicked. This file is also accessible through the systemname.KYP file folder,

125

located wherever the KYPIPE model was saved. The report is named systemname(Report), and

is in the form of a text file. The report displays the default parameters used in the simulation,

along with the selected sensor nodes and average travel time. This report for the example

simulation is shown in Figure 78.

Figure 78: Sensor Placement Summary Report

Also located in the systemname.KYP file folder, the file named systemname in the WQC file

format displays more useful information about the simulation. This text document displays the

optimal sensor locations throughout the course of the simulation, along with the average travel

time. The first sensors listed were the first combination tried in the simulation. These sensors

were replaced when a set of sensor locations was tried that resulted in a lower detection time.

The file shows the chronological order of optimal sensors as the simulation progressed. The

sensors on the bottom row are the optimal sensor locations chosen. This file is shown in Figure

79.

126

Figure 79: WQC File

When the sensor placement tool was executed, an Excel file with the file name

systemnameTimeMatrix.csv was generated. The file includes all data used to determine the

sensor location with the lowest time to detection. The first column shows all possible sensor

nodes, and the first row of nodes represents the injection nodes. The values show the travel times

between the injection node and sensor nodes (in minutes). If the cell shows 0 for a travel time,

this means that the sensor nodes are too far away from the injection nodes and the contaminant

will not reach the sensor within 24 hours. Therefore, the travel time is considered to be 24 hours

for calculation purposes. This file can be accessed in the systemname.KYP folder, and a sample

of the time matrix file is shown in Figure 80.

127

Figure 80: Time Matrix Excel File

128

Appendix E

Results of Sensor Placement Simulations

129

E.1 Results for Placement of One Sensor

Table 11: KYPIPE and TEVA-SPOT Sensor Placement Results (1 sensor)

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

KY 1

Fixed

Amount

4000 1 J-406 939.84 J-406 15.39 923.4 KYPIPE 16.4

2000 2 J-406 944.26 J-406 15.45 927.0 KYPIPE 17.3

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

500 8 J-406 971.31 J-737 15.83 949.8 KYPIPE 21.5

250 16 J-406 988.53 J-406 16.14 968.4 KYPIPE 20.1

Fixed

Rate

1000 1 J-406 956.80 J-406 15.66 939.6 KYPIPE 17.2

1000 2 J-406 956.56 J-737 15.65 939.0 KYPIPE 17.6

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1000 8 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1000 16 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

Fixed

Time

600 4 J-406 968.61 J-406 15.74 944.4 KYPIPE 24.2

800 4 J-406 961.84 J-406 15.53 931.8 KYPIPE 30.0

1000 4 J-406 955.45 J-406 15.5 930.0 KYPIPE 25.5

1200 4 J-406 952.13 J-406 15.49 929.4 KYPIPE 22.7

1400 4 J-406 948.20 J-406 15.48 928.8 KYPIPE 19.4

KY 2

Fixed

Amount

4000 1 J-138 783.54 J-485 12.72 763.2 KYPIPE 20.3

2000 2 J-138 782.43 J-485 12.98 778.8 KYPIPE 3.6

1000 4 J-139 789.61 J-485 13.22 793.2 TEVA-SPOT 3.6

500 8 J-485 795.58 J-138 13.59 815.4 TEVA-SPOT 19.8

250 16 J-485 831.60 J-139 14.17 850.2 TEVA-SPOT 18.6

Fixed

Rate

1000 1 J-138 841.32 J-485 13.26 795.6 KYPIPE 45.7

1000 2 J-485 832.72 J-485 13.22 793.2 KYPIPE 39.5

1000 4 J-139 789.61 J-485 13.22 793.2 TEVA-SPOT 3.6

1000 8 J-485 736.80 J-485 13.22 793.2 TEVA-SPOT 56.4

1000 16 J-485 715.25 J-485 13.22 793.2 TEVA-SPOT 78.0

Fixed

Time

600 4 J-485 831.40 J-485 13.49 809.4 KYPIPE 22.0

800 4 J-485 814.60 J-485 13.36 801.6 KYPIPE 13.0

1000 4 J-139 789.61 J-485 13.22 793.2 TEVA-SPOT 3.6

1200 4 J-139 767.96 J-485 13.09 785.4 TEVA-SPOT 17.4

1400 4 J-139 754.91 J-485 13.03 781.8 TEVA-SPOT 26.9

KY 3 Fixed 4000 1 J-225 802.39 J-225 12.96 777.6 KYPIPE 24.8

130

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

Amount 2000 2 J-225 803.83 J-225 12.97 778.2 KYPIPE 25.6

1000 4 J-225 815.89 J-225 13.07 784.2 KYPIPE 31.7

500 8 J-225 827.66 J-225 13.27 796.2 KYPIPE 31.5

250 16 J-225 842.87 J-225 13.48 808.8 KYPIPE 34.1

Fixed

Rate

1000 1 J-225 815.89 J-225 13.09 785.4 KYPIPE 30.5

1000 2 J-225 815.89 J-225 13.08 784.8 KYPIPE 31.1

1000 4 J-225 815.89 J-225 13.07 784.2 KYPIPE 31.7

1000 8 J-225 815.89 J-225 13.07 784.2 KYPIPE 31.7

1000 16 J-225 815.89 J-225 13.07 784.2 KYPIPE 31.7

Fixed

Time

600 4 J-225 817.03 J-225 13.1 786.0 KYPIPE 31.0

800 4 J-225 816.17 J-225 13.09 785.4 KYPIPE 30.8

1000 4 J-225 815.89 J-225 13.07 784.2 KYPIPE 31.7

1200 4 J-225 815.02 J-225 12.99 779.4 KYPIPE 35.6

1400 4 J-225 810.14 J-225 12.98 778.8 KYPIPE 31.3

KY 4

Fixed

Amount

4000 1 J-256 773.70 J-256 12.54 752.4 KYPIPE 21.3

2000 2 J-256 779.29 J-256 12.63 757.8 KYPIPE 21.5

1000 4 J-256 787.41 J-256 12.75 765.0 KYPIPE 22.4

500 8 J-256 801.70 J-256 12.92 775.2 KYPIPE 26.5

250 16 J-256 843.48 J-256 13.64 818.4 KYPIPE 25.1

Fixed

Rate

1000 1 J-256 792.68 J-256 12.85 771.0 KYPIPE 21.7

1000 2 J-256 787.59 J-256 12.78 766.8 KYPIPE 20.8

1000 4 J-256 787.41 J-256 12.75 765.0 KYPIPE 22.4

1000 8 J-256 786.34 J-256 12.73 763.8 KYPIPE 22.5

1000 16 J-256 786.34 J-256 12.73 763.8 KYPIPE 22.5

Fixed

Time

600 4 J-256 797.68 J-256 12.84 770.4 KYPIPE 27.3

800 4 J-256 791.79 J-256 12.78 766.8 KYPIPE 25.0

1000 4 J-256 787.41 J-256 12.75 765.0 KYPIPE 22.4

1200 4 J-256 785.45 J-256 12.73 763.8 KYPIPE 21.6

1400 4 J-256 783.75 J-223 12.63 757.8 KYPIPE 25.9

KY 5

Fixed

Amount

4000 1 J-321 545.85 J-13 9.26 555.6 TEVA-SPOT 9.8

2000 2 J-13 544.98 J-13 9.27 556.2 TEVA-SPOT 11.2

1000 4 J-299 553.65 J-13 9.29 557.4 TEVA-SPOT 3.8

500 8 J-299 577.04 J-321 9.55 573.0 KYPIPE 4.0

250 16 J-299 590.04 J-321 9.78 586.8 KYPIPE 3.2

Fixed

Rate

1000 1 J-299 560.58 J-13 9.38 562.8 TEVA-SPOT 2.2

1000 2 J-299 554.73 J-13 9.3 558.0 TEVA-SPOT 3.3

131

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

1000 4 J-299 553.65 J-13 9.29 557.4 TEVA-SPOT 3.8

1000 8 J-299 551.05 J-13 9.29 557.4 TEVA-SPOT 6.4

1000 16 J-299 549.31 J-13 9.29 557.4 TEVA-SPOT 8.1

Fixed

Time

600 4 J-299 567.29 J-13 9.39 563.4 KYPIPE 3.9

800 4 J-299 557.76 J-13 9.37 562.2 TEVA-SPOT 4.4

1000 4 J-299 553.65 J-13 9.29 557.4 TEVA-SPOT 3.8

1200 4 J-299 553.43 J-13 9.28 556.8 TEVA-SPOT 3.4

1400 4 J-13 549.96 J-13 9.28 556.8 TEVA-SPOT 6.8

KY 6

Fixed

Amount

4000 1 J-114 709.40 J-114 11.32 679.2 KYPIPE 30.2

2000 2 J-114 699.78 J-114 11.17 670.2 KYPIPE 29.6

1000 4 J-114 696.69 J-114 11.05 663.0 KYPIPE 33.7

500 8 J-114 695.05 J-114 11.04 662.4 KYPIPE 32.7

250 16 J-114 718.70 J-114 11.42 685.2 KYPIPE 33.5

Fixed

Rate

1000 1 J-114 727.01 J-114 11.4 684.0 KYPIPE 43.0

1000 2 J-114 709.57 J-114 11.21 672.6 KYPIPE 37.0

1000 4 J-114 696.69 J-114 11.05 663.0 KYPIPE 33.7

1000 8 J-114 688.04 J-114 10.99 659.4 KYPIPE 28.6

1000 16 J-114 688.04 J-114 10.99 659.4 KYPIPE 28.6

Fixed

Time

600 4 J-114 703.70 J-114 11.09 665.4 KYPIPE 38.3

800 4 J-114 698.48 J-114 11.06 663.6 KYPIPE 34.9

1000 4 J-114 696.69 J-114 11.05 663.0 KYPIPE 33.7

1200 4 J-114 696.36 J-114 11.04 662.4 KYPIPE 34.0

1400 4 J-114 692.28 J-114 11.03 661.8 KYPIPE 30.5

KY 7

Fixed

Amount

4000 1 J-271 856.67 J-56 14.95 897.0 TEVA-SPOT 40.3

2000 2 J-271 871.50 J-271 15.07 904.2 TEVA-SPOT 32.7

1000 4 J-271 894.50 J-271 15.28 916.8 TEVA-SPOT 22.3

500 8 J-271 914.67 J-271 15.47 928.2 TEVA-SPOT 13.5

250 16 J-249 912.50 J-271 15.79 947.4 TEVA-SPOT 34.9

Fixed

Rate

1000 1 J-271 894.50 J-271 15.35 921.0 TEVA-SPOT 26.5

1000 2 J-271 894.50 J-271 15.35 921.0 TEVA-SPOT 26.5

1000 4 J-271 894.50 J-271 15.28 916.8 TEVA-SPOT 22.3

1000 8 J-271 894.50 J-271 15.24 914.4 TEVA-SPOT 19.9

1000 16 J-249 876.17 J-271 15.2 912.0 TEVA-SPOT 35.8

Fixed

Time

600 4 J-271 911.83 J-271 15.41 924.6 TEVA-SPOT 12.8

800 4 J-271 904.67 J-271 15.38 922.8 TEVA-SPOT 18.1

1000 4 J-271 894.50 J-271 15.28 916.8 TEVA-SPOT 22.3

132

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

1200 4 J-271 891.00 J-271 15.22 913.2 TEVA-SPOT 22.2

1400 4 J-271 882.50 J-271 15.14 908.4 TEVA-SPOT 25.9

KY 8

Fixed

Amount

4000 1 J-540 1017.01 J-541 17.64 1058.4 TEVA-SPOT 41.4

2000 2 J-574 991.79 J-541 17.18 1030.8 TEVA-SPOT 39.0

1000 4 J-574 985.16 J-541 17.23 1033.8 TEVA-SPOT 48.6

500 8 J-574 1004.73 J-949 17.21 1032.6 TEVA-SPOT 27.9

250 16 J-545 1034.34 J-949 17.39 1043.4 TEVA-SPOT 9.1

Fixed

Rate

1000 1 J-574 1053.92 J-540 17.99 1079.4 TEVA-SPOT 25.5

1000 2 J-574 1007.65 J-541 17.29 1037.4 TEVA-SPOT 29.7

1000 4 J-574 985.16 J-541 17.23 1033.8 TEVA-SPOT 48.6

1000 8 J-574 983.03 J-949 16.98 1018.8 TEVA-SPOT 35.8

1000 16 J-574 982.97 J-949 16.92 1015.2 TEVA-SPOT 32.2

Fixed

Time

600 4 J-574 997.30 J-756 17.36 1041.6 TEVA-SPOT 44.3

800 4 J-574 990.13 J-541 17.3 1038.0 TEVA-SPOT 47.9

1000 4 J-574 985.16 J-541 17.23 1033.8 TEVA-SPOT 48.6

1200 4 J-574 980.77 J-949 17.19 1031.4 TEVA-SPOT 50.6

1400 4 J-574 978.98 J-949 17.15 1029.0 TEVA-SPOT 50.0

KY 9

Fixed

Amount

4000 1 J-814 1369.81 J-563 22.66 1359.6 KYPIPE 10.2

2000 2 J-814 1369.91 J-563 22.66 1359.6 KYPIPE 10.3

1000 4 J-814 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

500 8 J-814 1369.72 J-563 22.66 1359.6 KYPIPE 10.1

250 16 J-708 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

Fixed

Rate

1000 1 J-814 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

1000 2 J-814 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

1000 4 J-814 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

1000 8 J-814 1369.44 J-563 22.66 1359.6 KYPIPE 9.8

1000 16 J-814 1369.44 J-563 22.66 1359.6 KYPIPE 9.8

Fixed

Time

600 4 J-1083 1370.19 J-563 22.66 1359.6 KYPIPE 10.6

800 4 J-1083 1370.19 J-563 22.66 1359.6 KYPIPE 10.6

1000 4 J-814 1370.09 J-563 22.66 1359.6 KYPIPE 10.5

1200 4 J-814 1370.00 J-563 22.66 1359.6 KYPIPE 10.4

1400 4 J-814 1369.91 J-563 22.66 1359.6 KYPIPE 10.3

KY 10 Fixed

Amount

4000 1 J-321 992.15 J-321 16.49 989.4 KYPIPE 2.8

2000 2 J-321 993.21 J-321 16.5 990.0 KYPIPE 3.2

1000 4 J-321 995.02 J-321 16.48 988.8 KYPIPE 6.2

500 8 J-321 1000.38 J-321 16.52 991.2 KYPIPE 9.2

133

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

250 16 J-321 1040.57 J-321 16.58 994.8 KYPIPE 45.8

Fixed

Rate

1000 1 J-321 995.02 J-321 16.49 989.4 KYPIPE 5.6

1000 2 J-321 995.02 J-321 16.49 989.4 KYPIPE 5.6

1000 4 J-321 995.02 J-321 16.48 988.8 KYPIPE 6.2

1000 8 J-321 995.02 J-321 16.47 988.2 KYPIPE 6.8

1000 16 J-321 995.02 J-321 16.47 988.2 KYPIPE 6.8

Fixed

Time

600 4 J-321 999.33 J-321 16.49 989.4 KYPIPE 9.9

800 4 J-321 996.08 J-321 16.48 988.8 KYPIPE 7.3

1000 4 J-321 995.02 J-321 16.48 988.8 KYPIPE 6.2

1200 4 J-321 994.35 J-321 16.46 987.6 KYPIPE 6.8

1400 4 J-321 993.97 J-321 16.46 987.6 KYPIPE 6.4

KY 11

Fixed

Amount

4000 1 J-21 1310.49 J-539 20.95 1257.0 KYPIPE 53.5

2000 2 J-93 1301.99 J-731 20.93 1255.8 KYPIPE 46.2

1000 4 J-93 1302.11 J-771 20.94 1256.4 KYPIPE 45.7

500 8 J-93 1301.86 J-731 20.99 1259.4 KYPIPE 42.5

250 16 J-93 1316.07 J-731 21.01 1260.6 KYPIPE 55.5

Fixed

Rate

1000 1 J-93 1315.94 J-539 20.99 1259.4 KYPIPE 56.5

1000 2 J-93 1303.26 J-731 20.96 1257.6 KYPIPE 45.7

1000 4 J-93 1302.11 J-771 20.94 1256.4 KYPIPE 45.7

1000 8 J-93 1299.07 J-771 20.94 1256.4 KYPIPE 42.7

1000 16 J-93 1299.07 J-771 20.94 1256.4 KYPIPE 42.7

Fixed

Time

600 4 J-93 1303.00 J-771 20.95 1257.0 KYPIPE 46.0

800 4 J-93 1302.24 J-771 20.94 1256.4 KYPIPE 45.8

1000 4 J-93 1302.11 J-771 20.94 1256.4 KYPIPE 45.7

1200 4 J-93 1301.35 J-731 20.96 1257.6 KYPIPE 43.7

1400 4 J-93 1300.97 J-731 20.96 1257.6 KYPIPE 43.4

KY 12

Fixed

Amount

4000 1 J-1069 1227.61 J-610 20.29 1217.4 KYPIPE 10.2

2000 2 J-1069 1230.31 J-610 20.34 1220.4 KYPIPE 9.9

1000 4 J-1255 1236.31 J-1469 20.4 1224.0 KYPIPE 12.3

500 8 J-1469 1237.17 J-610 20.43 1225.8 KYPIPE 11.4

250 16 J-1469 1248.07 J-1469 20.61 1236.6 KYPIPE 11.5

Fixed

Rate

1000 1 J-121 1281.55 J-695 21.01 1260.6 KYPIPE 20.9

1000 2 J-1255 1238.62 J-610 20.46 1227.6 KYPIPE 11.0

1000 4 J-1255 1236.31 J-1469 20.4 1224.0 KYPIPE 12.3

1000 8 J-1469 1230.81 J-610 20.35 1221.0 KYPIPE 9.8

1000 16 J-1469 1230.02 J-610 20.34 1220.4 KYPIPE 9.6

134

TEVA-SPOT KYPIPE

System

Injection

Rate

(mg/min)

Injectio

n Time

(hours)

Sensor

Node

Time to

Detection

(min)

Sensor

Node

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest Time

to Detection

Difference

in min

(Higher

Time-Lower

Time)

Fixed

Time

600 4 J-211 1243.92 J-1469 20.53 1231.8 KYPIPE 12.1

800 4 J-211 1240.33 J-1469 20.45 1227.0 KYPIPE 13.3

1000 4 J-1255 1236.31 J-1469 20.4 1224.0 KYPIPE 12.3

1200 4 J-1069 1234.66 J-610 20.37 1222.2 KYPIPE 12.5

1400 4 J-1469 1232.85 J-610 20.35 1221.0 KYPIPE 11.8

E.2 Results for Placement of Two Sensors

Table 12: KYPIPE and TEVA-SPOT Sensor Placement Results (2 sensors)

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

KY

1

Fixed Amou

nt

4000 1 J-235 J-497 817.25 J-235 J-497 13.31 798.6 KYPIPE 18.7

2000 2 J-235 J-497 822.17 J-235 J-497 13.38 802.8 KYPIPE 19.4

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

500 8 J-245 J-406 843.81 J-245 J-406 13.68 820.8 KYPIPE 23.0

250 16 J-244 J-406 852.30 J-244 J-406 13.78 826.8 KYPIPE 25.5

Fixed Rate

1000 1 J-245 J-406 838.65 J-235 J-497 13.63 817.8 KYPIPE 20.8

1000 2 J-245 J-406 838.40 J-235 J-497 13.49 809.4 KYPIPE 29.0

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1000 8 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1000 16 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

Fixed Time

600 4 J-245 J-406 843.20 J-235 J-497 13.59 815.4 KYPIPE 27.8

800 4 J-244 J-406 839.39 J-235 J-497 13.48 808.8 KYPIPE 30.6

1000 4 J-235 J-497 834.47 J-235 J-497 13.45 807.0 KYPIPE 27.5

1200 4 J-235 J-497 825.74 J-235 J-497 13.43 805.8 KYPIPE 19.9

1400 4 J-235 J-497 824.14 J-235 J-497 13.41 804.6 KYPIPE 19.5

KY

2

Fixed

Amount

4000 1 J-138 J-636 529.68 J-485 J-534 7.8 468.0 KYPIPE 61.7

2000 2 J-138 J-636 542.33 J-485 J-534 7.87 472.2 KYPIPE 70.1

1000 4 J-485 J-534 555.89 J-485 J-534 8.11 486.6 KYPIPE 69.3

500 8 J-485 J-534 568.53 J-485 J-534 8.42 505.2 KYPIPE 63.3

250 16 J-485 J-534 595.55 J-138 J-534 9.05 543.0 KYPIPE 52.5

Fixed 1000 1 J-138 J-534 583.31 J-485 J-534 8.2 492.0 KYPIPE 91.3

135

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

Rate 1000 2 J-485 J-60 568.53 J-485 J-534 8.14 488.4 KYPIPE 80.1

1000 4 J-485 J-534 555.89 J-485 J-534 8.11 486.6 KYPIPE 69.3

1000 8 J-138 J-534 537.27 J-485 J-534 8.06 483.6 KYPIPE 53.7

1000 16 J-485 J-534 519.87 J-485 J-534 8.05 483.0 KYPIPE 36.9

Fixed

Time

600 4 J-485 J-534 576.93 J-485 J-534 8.44 506.4 KYPIPE 70.5

800 4 J-485 J-534 561.65 J-485 J-534 8.27 496.2 KYPIPE 65.5

1000 4 J-485 J-534 555.89 J-485 J-534 8.11 486.6 KYPIPE 69.3

1200 4 J-485 J-534 548.50 J-485 J-534 7.96 477.6 KYPIPE 70.9

1400 4 J-138 J-534 545.67 J-485 J-534 7.92 475.2 KYPIPE 70.5

KY

3

Fixed

Amount

4000 1 J-2 J-225 577.90 J-2 J-225 9.11 546.6 KYPIPE 31.3

2000 2 J-2 J-225 571.87 J-2 J-225 8.97 538.2 KYPIPE 33.7

1000 4 J-2 J-225 575.02 J-2 J-225 8.86 531.6 KYPIPE 43.4

500 8 J-2 J-225 586.51 J-2 J-225 9.03 541.8 KYPIPE 44.7

250 16 J-2 J-225 606.32 J-2 J-225 9.25 555.0 KYPIPE 51.3

Fixed Rate

1000 1 J-2 J-225 595.41 J-2 J-225 9.22 553.2 KYPIPE 42.2

1000 2 J-2 J-225 587.66 J-2 J-225 9.08 544.8 KYPIPE 42.9

1000 4 J-2 J-225 575.02 J-2 J-225 8.86 531.6 KYPIPE 43.4

1000 8 J-2 J-225 573.59 J-2 J-225 8.86 531.6 KYPIPE 42.0

1000 16 J-2 J-225 573.59 J-2 J-225 8.86 531.6 KYPIPE 42.0

Fixed Time

600 4 J-2 J-225 577.03 J-2 J-225 8.91 534.6 KYPIPE 42.4

800 4 J-2 J-225 575.89 J-2 J-225 8.89 533.4 KYPIPE 42.5

1000 4 J-2 J-225 575.02 J-2 J-225 8.86 531.6 KYPIPE 43.4

1200 4 J-2 J-225 574.16 J-2 J-225 8.8 528.0 KYPIPE 46.2

1400 4 J-2 J-225 569.00 J-2 J-225 8.79 527.4 KYPIPE 41.6

KY

4

Fixed Amou

nt

4000 1 J-475 J-610 718.93 J-475 J-610 11.73 703.8 KYPIPE 15.1

2000 2 J-475 J-610 722.41 J-475 J-610 11.76 705.6 KYPIPE 16.8

1000 4 J-475 J-610 730.27 J-475 J-610 11.85 711.0 KYPIPE 19.3

500 8 J-475 J-610 740.80 J-475 J-610 11.87 712.2 KYPIPE 28.6

250 16 J-475 J-610 757.86 J-256 J-641 12.26 735.6 KYPIPE 22.3

Fixed Rate

1000 1 J-475 J-641 735.54 J-475 J-641 11.91 714.6 KYPIPE 20.9

1000 2 J-475 J-610 730.36 J-475 J-641 11.88 712.8 KYPIPE 17.6

1000 4 J-475 J-610 730.27 J-475 J-610 11.85 711.0 KYPIPE 19.3

1000 8 J-475 J-610 728.66 J-475 J-610 11.81 708.6 KYPIPE 20.1

1000 16 J-475 J-610 728.66 J-475 J-610 11.81 708.6 KYPIPE 20.1

Fixed 600 4 J-475 J-610 738.75 J-475 J-610 11.89 713.4 KYPIPE 25.3

136

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

Time 800 4 J-475 J-610 733.13 J-475 J-610 11.86 711.6 KYPIPE 21.5

1000 4 J-475 J-610 730.27 J-475 J-610 11.85 711.0 KYPIPE 19.3

1200 4 J-475 J-610 729.11 J-475 J-610 11.82 709.2 KYPIPE 19.9

1400 4 J-475 J-610 726.79 J-475 J-610 11.81 708.6 KYPIPE 18.2

KY

5

Fixed

Amou

nt

4000 1 J-247 J-321 472.42 J-247 J-299 8.08 484.8 TEVA-

SPOT 12.4

2000 2 J-247 J-321 469.39 J-247 J-299 8.11 486.6 TEVA-

SPOT 17.2

1000 4 J-159 J-321 478.27 J-13 J-247 8.13 487.8 TEVA-SPOT

9.5

500 8 J-160 J-321 491.26 J-247 J-299 8.28 496.8 TEVA-

SPOT 5.5

250 16 J-112 J-321 503.39 J-159 J-321 8.42 505.2 TEVA-

SPOT 1.8

Fixed

Rate

1000 1 J-247 J-321 486.07 J-247 J-299 8.22 493.2 TEVA-SPOT

7.1

1000 2 J-247 J-321 481.08 J-13 J-247 8.17 490.2 TEVA-

SPOT 9.1

1000 4 J-159 J-321 478.27 J-13 J-247 8.13 487.8 TEVA-

SPOT 9.5

1000 8 J-159 J-321 475.24 J-13 J-247 8.13 487.8 TEVA-SPOT

12.6

1000 16 J-159 J-321 475.24 J-13 J-247 8.13 487.8 TEVA-

SPOT 12.6

Fixed

Time

600 4 J-159 J-321 484.98 J-13 J-247 8.18 490.8 TEVA-

SPOT 5.8

800 4 J-159 J-321 480.65 J-247 J-299 8.15 489.0 TEVA-SPOT

8.4

1000 4 J-159 J-321 478.27 J-13 J-247 8.13 487.8 TEVA-

SPOT 9.5

1200 4 J-159 J-321 477.83 J-247 J-299 8.1 486.0 TEVA-SPOT

8.2

1400 4 J-321 J-376 476.32 J-247 J-299 8.1 486.0 TEVA-

SPOT 9.7

KY

6

Fixed

Amou

nt

4000 1 J-114 J-476 620.87 J-114 J-365 9.89 593.4 KYPIPE 27.5

2000 2 J-114 J-130 608.97 J-114 J-130 9.73 583.8 KYPIPE 25.2

1000 4 J-114 J-130 602.12 J-114 J-130 9.58 574.8 KYPIPE 27.3

500 8 J-114 J-130 598.70 J-114 J-130 9.55 573.0 KYPIPE 25.7

250 16 J-130 J-398 619.40 J-114 J-130 9.8 588.0 KYPIPE 31.4

Fixed

Rate

1000 1 J-114 J-130 635.22 J-114 J-365 9.92 595.2 KYPIPE 40.0

1000 2 J-114 J-130 617.61 J-114 J-130 9.75 585.0 KYPIPE 32.6

1000 4 J-114 J-130 602.12 J-114 J-130 9.58 574.8 KYPIPE 27.3

1000 8 J-114 J-130 593.48 J-114 J-130 9.53 571.8 KYPIPE 21.7

1000 16 J-114 J-130 593.48 J-114 J-130 9.52 571.2 KYPIPE 22.3

Fixed Time

600 4 J-114 J-130 608.15 J-114 J-130 9.6 576.0 KYPIPE 32.2

800 4 J-114 J-130 604.24 J-114 J-130 9.59 575.4 KYPIPE 28.8

1000 4 J-114 J-130 602.12 J-114 J-130 9.58 574.8 KYPIPE 27.3

137

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

1200 4 J-114 J-130 601.96 J-114 J-130 9.66 579.6 KYPIPE 22.4

1400 4 J-114 J-130 600.98 J-114 J-130 9.66 579.6 KYPIPE 21.4

KY

7

Fixed Amou

nt

4000 1 J-49 J-9 580.33 J-13 J-15 11.5 690.0 TEVA-SPOT

109.7

2000 2 J-249 J-9 582.67 J-13 J-15 11.52 691.2 TEVA-

SPOT 108.5

1000 4 J-441 J-9 585.00 J-13 J-15 11.62 697.2 TEVA-

SPOT 112.2

500 8 J-81 J-9 587.67 J-13 J-15 11.7 702.0 TEVA-SPOT

114.3

250 16 J-249 J-9 600.00 J-13 J-15 11.9 714.0 TEVA-

SPOT 114.0

Fixed

Rate

1000 1 J-81 J-9 587.00 J-15 J-9 11.65 699.0 TEVA-

SPOT 112.0

1000 2 J-81 J-9 587.00 J-15 J-9 11.65 699.0 TEVA-SPOT

112.0

1000 4 J-441 J-9 585.00 J-13 J-15 11.62 697.2 TEVA-

SPOT 112.2

1000 8 J-441 J-9 581.00 J-13 J-15 11.61 696.6 TEVA-

SPOT 115.6

1000 16 J-441 J-9 581.00 J-13 J-15 11.61 696.6 TEVA-SPOT

115.6

Fixed Time

600 4 J-81 J-9 590.67 J-13 J-15 11.69 701.4 TEVA-

SPOT 110.7

800 4 J-81 J-9 588.17 J-13 J-15 11.64 698.4 TEVA-

SPOT 110.2

1000 4 J-441 J-9 585.00 J-13 J-15 11.62 697.2 TEVA-SPOT

112.2

1200 4 J-81 J-9 581.17 J-13 J-15 11.61 696.6 TEVA-

SPOT 115.4

1400 4 J-441 J-9 578.67 J-13 J-15 11.54 692.4 TEVA-SPOT

113.7

KY

8

Fixed

Amou

nt

4000 1 J-540 J-576 879.56 J-541 J-890 14.78 886.8 TEVA-

SPOT 7.2

2000 2 J-574 J-576 871.79 J-574 J-576 14.61 876.6 TEVA-

SPOT 4.8

1000 4 J-296 J-576 870.33 J-1035 J-756 14.62 877.2 TEVA-SPOT

6.9

500 8 J-455 J-576 877.70 J-451 J-756 14.74 884.4 TEVA-

SPOT 6.7

250 16 J-545 J-576 897.21 J-103 J-565 14.94 896.4 KYPIPE 0.8

Fixed Rate

1000 1 J-574 J-576 897.01 J-540 J-576 15.08 904.8 TEVA-

SPOT 7.8

1000 2 J-545 J-576 879.76 J-541 J-576 14.72 883.2 TEVA-

SPOT 3.4

1000 4 J-296 J-576 870.33 J-1035 J-756 14.62 877.2 TEVA-SPOT

6.9

1000 8 J-545 J-576 865.75 J-1035 J-574 14.61 876.6 TEVA-

SPOT 10.8

1000 16 J-545 J-576 865.75 J-1035 J-574 14.57 874.2 TEVA-

SPOT 8.4

Fixed

Time

600 4 J-545 J-576 878.56 J-1035 J-756 14.71 882.6 TEVA-SPOT

4.0

800 4 J-545 J-576 871.86 J-1035 J-756 14.65 879.0 TEVA-

SPOT 7.1

1000 4 J-296 J-576 870.33 J-1035 J-756 14.62 877.2 TEVA- 6.9

138

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

SPOT

1200 4 J-574 J-576 986.21 J-1035 J-574 14.57 874.2 KYPIPE 112.0

1400 4 J-574 J-576 867.15 J-1035 J-574 14.57 874.2 TEVA-

SPOT 7.1

KY

9

Fixed

Amou

nt

4000 1 J-589 J-814 1310.37 J-563 J-589 21.59 1295.4 KYPIPE 15.0

2000 2 J-588 J-814 1310.56 J-563 J-589 21.6 1296.0 KYPIPE 14.6

1000 4 J-588 J-814 1310.83 J-563 J-589 21.6 1296.0 KYPIPE 14.8

500 8 J-588 J-814 1311.02 J-563 J-589 21.6 1296.0 KYPIPE 15.0

250 16 J-588 J-814 1310.65 J-563 J-589 21.61 1296.6 KYPIPE 14.1

Fixed

Rate

1000 1 J-588 J-814 1310.93 J-563 J-589 21.6 1296.0 KYPIPE 14.9

1000 2 J-588 J-814 1310.83 J-563 J-589 21.6 1296.0 KYPIPE 14.8

1000 4 J-588 J-814 1310.83 J-563 J-589 21.6 1296.0 KYPIPE 14.8

1000 8 J-588 J-814 1310.19 J-563 J-589 21.6 1296.0 KYPIPE 14.2

1000 16 J-588 J-814 1309.07 J-563 J-589 21.6 1296.0 KYPIPE 13.1

Fixed

Time

600 4 J-588 J-814 1311.39 J-563 J-589 21.6 1296.0 KYPIPE 15.4

800 4 J-588 J-814 1311.02 J-563 J-589 21.6 1296.0 KYPIPE 15.0

1000 4 J-588 J-814 1310.83 J-563 J-589 21.6 1296.0 KYPIPE 14.8

1200 4 J-588 J-814 1310.74 J-563 J-589 21.6 1296.0 KYPIPE 14.7

1400 4 J-588 J-814 1310.65 J-563 J-589 21.6 1296.0 KYPIPE 14.7

KY

10

Fixed

Amou

nt

4000 1 J-321 J-592 896.46 J-11 J-321 14.43 865.8 KYPIPE 30.7

2000 2 J-321 J-592 897.61 J-11 J-321 14.37 862.2 KYPIPE 35.4

1000 4 J-321 J-592 899.43 J-11 J-321 14.25 855.0 KYPIPE 44.4

500 8 J-321 J-592 903.64 J-11 J-321 14.29 857.4 KYPIPE 46.2

250 16 J-321 J-741 922.87 J-11 J-321 14.37 862.2 KYPIPE 60.7

Fixed

Rate

1000 1 J-321 J-592 899.43 J-11 J-321 14.46 867.6 KYPIPE 31.8

1000 2 J-321 J-592 899.43 J-11 J-321 14.41 864.6 KYPIPE 34.8

1000 4 J-321 J-592 899.43 J-11 J-321 14.25 855.0 KYPIPE 44.4

1000 8 J-321 J-592 899.43 J-11 J-321 14.23 853.8 KYPIPE 45.6

1000 16 J-321 J-592 899.43 J-11 J-321 14.23 853.8 KYPIPE 45.6

Fixed

Time

600 4 J-321 J-592 902.49 J-11 J-321 14.29 857.4 KYPIPE 45.1

800 4 J-321 J-592 900.57 J-11 J-321 14.27 856.2 KYPIPE 44.4

1000 4 J-321 J-592 899.43 J-11 J-321 14.25 855.0 KYPIPE 44.4

1200 4 J-321 J-592 898.76 J-11 J-321 14.24 854.4 KYPIPE 44.4

1400 4 J-321 J-592 898.37 J-11 J-321 14.24 854.4 KYPIPE 44.0

KY

11

Fixed Amou

nt

4000 1 J-338 J-739 1235.90 J-242 J-539 19.67 1180.2 KYPIPE 55.7

2000 2 J-338 J-739 1226.64 J-242 J-731 19.64 1178.4 KYPIPE 48.2

139

TEVA-SPOT KYPIPE

Sys-

tem

Injectio

n Rate

(mg/mi

n)

Injectio

n Time

(hours)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (min)

Sensor

Node

#1

Sensor

Node

#2

Time to

Detectio

n (hr)

Time to

Detectio

n (min)

Fastest

Time to

Detectio

n

Differenc

e in min

(Higher -

Lower

Time)

1000 4 J-338 J-739 1227.15 J-242 J-731 19.67 1180.2 KYPIPE 47.0

500 8 J-338 J-739 1228.79 J-242 J-731 19.71 1182.6 KYPIPE 46.2

250 16 J-21 J-417 1244.90 J-242 J-731 19.73 1183.8 KYPIPE 61.1

Fixed

Rate

1000 1 J-338 J-739 1240.34 J-242 J-539 19.68 1180.8 KYPIPE 59.5

1000 2 J-338 J-739 1227.15 J-242 J-731 19.66 1179.6 KYPIPE 47.6

1000 4 J-338 J-739 1227.15 J-242 J-731 19.67 1180.2 KYPIPE 47.0

1000 8 J-338 J-739 1227.15 J-242 J-731 19.66 1179.6 KYPIPE 47.6

1000 16 J-338 J-739 1227.15 J-242 J-731 19.66 1179.6 KYPIPE 47.6

Fixed

Time

600 4 J-338 J-739 1228.54 J-242 J-731 19.66 1179.6 KYPIPE 48.9

800 4 J-338 J-739 1227.40 J-242 J-731 19.66 1179.6 KYPIPE 47.8

1000 4 J-338 J-739 1227.15 J-242 J-731 19.67 1180.2 KYPIPE 47.0

1200 4 J-338 J-739 1227.15 J-242 J-731 19.66 1179.6 KYPIPE 47.6

1400 4 J-338 J-739 1227.15 J-242 J-731 19.66 1179.6 KYPIPE 47.6

KY

12

Fixed Amou

nt

4000 1 J-182 J-211 1178.88 J-182 J-211 19.48 1168.8 KYPIPE 10.1

2000 2 J-182 J-211 1180.92 J-182 J-211 19.51 1170.6 KYPIPE 10.3

1000 4 J-182 J-211 1185.17 J-1469 J-182 19.52 1171.2 KYPIPE 14.0

500 8 J-1469 J-182 1181.09 J-1469 J-182 19.52 1171.2 KYPIPE 9.9

250 16 J-1469 J-182 1183.82 J-1469 J-182 19.53 1171.8 KYPIPE 12.0

Fixed Rate

1000 1 J-183 J-211 1192.98 J-182 J-211 19.65 1179.0 KYPIPE 14.0

1000 2 J-182 J-211 1186.59 J-182 J-211 19.59 1175.4 KYPIPE 11.2

1000 4 J-182 J-211 1185.17 J-1469 J-182 19.52 1171.2 KYPIPE 14.0

1000 8 J-1469 J-182 1179.54 J-1596 J-48 19.48 1168.8 KYPIPE 10.7

1000 16 J-1469 J-182 1178.88 J-1596 J-48 19.47 1168.2 KYPIPE 10.7

Fixed

Time

600 4 J-183 J-211 1190.77 J-1469 J-182 19.63 1177.8 KYPIPE 13.0

800 4 J-182 J-211 1188.20 J-1469 J-182 19.56 1173.6 KYPIPE 14.6

1000 4 J-182 J-211 1185.17 J-1469 J-182 19.52 1171.2 KYPIPE 14.0

1200 4 J-182 J-211 1183.79 J-1469 J-182 19.52 1171.2 KYPIPE 12.6

1400 4 J-1469 J-182 1182.44 J-1469 J-182 19.51 1170.6 KYPIPE 11.8

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Sensor Placement Guidance in Small Water Distribution Systems25.3] Comparison of KYPIPE... · Sensor Placement Guidance in Small Water Distribution Systems Developed by the University