Studying Distribution System Hydraulics and Flow to ... Security Deliverables/HSD/Deliverable… ·...

Studying Distribution System Hydraulics and Flow Dynamics to Improve Water Utility Operational

Decision MakingUNIVERSITY OF KENTUCKY (Principal Investigator)

Lexington, KentuckyLindell Ormsbee, Sebastian Bryson, Scott Yost

UNIVERSITY OF CINCINNATI (Collaborating University)Cincinnati, Ohio

Jim Uber, Dominic Boccelli

UNIVERSITY OF MISSOURI (Collaborating University)Columbia, Missouri

Robert Reed

WESTERN KENTUCKY UNIVERSITY (Collaborating University)Bowling Green, Kentucky

Andrew Ernest 1

Meeting Agenda8:30 Welcome and University of Kentucky Security Research Highlights

Dr. Jim Tracy, Vice President for ResearchDr. Eric Grulke, Associate Dean for Research and Graduate Studies

8:45 Kentucky Critical Infrastructure Protection Program

Dr. Sam Varnado, NIHS

9:00 Homeland Security Perspective

Mr. John Laws, Department of Homeland Security

9:15 EPA Perspective

Dr. Robert Janke, WIPD WSEPA/NHSRC

9:30 Introduction of Advisory Board Members

9:45 Introduction of the Project Goals and Milestones

Dr. Lindell Ormsbee, University of Kentucky

10:15 Break 2

Meeting Agenda10:30 Presentations

Flow Distribution Model –Mr. Ben Albritton, University of Kentucky

Utility Section and Model Calibration –Mr. Joe Goodin, University of Kentucky

Physical Model –Mr. Matthew Jolly, University of Kentucky

Laboratory Tour Following Discussion

12:00 Lunch

3

Meeting Agenda1:30 Presentations

SCADA Survey – Dr. Robert Reed, University of Missouri

Real‐time Modeling – Dr. Jim Uber, University of Cincinnati

Sensor Placement –Ms. Amanda Lothes, UK

Toolkit and Expert Systems – Dr. Andrew Ernest, WKU

3:30 Break

3:45 Panel Discussion

4:30 Closing Remarks4

5


Decision Making

Project Overview: Lindell Ormsbee

Project Goal• To assist water utilities in improving the operation of their water distribution systems through a better understanding of the impact of water distribution system hydraulics and flow dynamics on operational decision making:– Normal operations

– Emergency operations• Natural events

• Man made events

6

Knowledge ToolsResearch

Water Distribution System Operations Hierarchy

7

Needs Assessment/Technology Gaps

• Gap 1: No synthesis document exists that provides a state‐of‐the‐art assessment and a state‐of‐the‐practice for SCADA systems across the drinking water industry.

• Objective 1: Develop a comprehensive report assessing the current state of SCADA systems (including hydraulic and water quality sensors) across the drinking water industry for use in support of real time operational modeling.

8


9

Needs Assessment/Technology Gaps• Gap 2: A simple modeling tool is needed to help utilities understand basic system hydraulics during normal operational flow conditions and also during abnormal flow patterns resulting from unanticipated events.

• Objective 2: Develop software that will provide a graphical representation of a water distribution system along with the flow directions in the pipes for a specified operating condition.

10


11

Needs Assessment/Technology Gaps• Gap 3: An understanding of the sensitivity of water quality measurements to variations in system flow dynamics is needed in order to be able to distinguish between possible incursions and operational fluctuations.

• Objective 3a. Develop laboratory scale model of medium sized utility water distribution system to evaluate the ability of existing software to adequately characterize the flow dynamics and water quality characteristics of the system.

• Objective 3b: Calibrate a large‐scale network model against a historical record of operational changes stored in SCADA, and use this model to understand the sensitivity of network flows and flow paths (and thus water quality) to changes in system demand and operation. 12


13

Needs Assessment/Technology Gaps

• Gap 4:Guidance is needed for optimal placement of hydraulic sensors in order to better assist utilities in understanding their system’s flow dynamics.

• Objective 4. Develop guidance for optimal placement of hydraulic sensors based on results of flow dynamics model and operational constraints of the utility.

14


15

Needs Assessment/Technology Gaps• Gap 5: Most utilities lack guidance with respect to how to use SCADA and modeling data in support of their system operations, and in particular with regard to responding to potential incursion events. Guidance is needed for optimal placement of hydraulic sensors in order to better assist utilities in understanding their system’s flow dynamics.

• Objective 5. Develop a decision‐support toolkit which will allow utilities to select the appropriate level of operational tools in support of their operational needs. 16


17

Project TasksTask #

1 E

Project Task1

stablishment of an Advisory Board

Corresponding Capability Gaps

2 Select Water Utility Partners

3 Survey and Evaluate SCADA Systems Gap #14 Build Laboratory Scale Hydraulic Model

of Selected Water Distribution SystemGap #2

5 Develop Graphical Flow Distribution Model

Gap #2

6 Develop and Calibrate Hydraulic and Wate

7 Qr Quality Computer Models

Gap #2

8 Develop Sensor

uantify Flow and Water Quality al‐Time Modeling

9 Develop Toolkit

Dynamics Through RePlacement Guidance

Gap #3

Gap #4Gap #5

10 Test and Evaluate Toolkit Gap #511 Validate Toolkit Gap #5

12 Write Report Gap #5

Initial Award

Year One

18

University of Missouri

KYPIPE LLC

University of Cincinnati

Western Kentucky University

University of Kentucky

Participant ResponsibilitiesTask T#1 T#2 T#3 T#4 T#5 T#6 T#7 T#8 T#9 T#10 T#11 T#12

University of Kentucky Investigators

Lindell Ormsbee L. Sebastian Bryson

Lts

Lts

asts

asts

ts Lfs

tsfs

tsL

asts

asts

asL

Lts

Scott L tsYostWestern Kentucky University

Andrew Ernest ts ts L L ts tsUniversity of Missouri

Robert Reed ts ts L ts ts tsUniversity of Cincinnati

James Uber ts ts L ts ts tsKYPIPE LLC

Doug Wood L ts ts

20

L = Task Lead; as = Administrative Support; ts = Technical Support; fs = Field Support

Project TasksTask #

1 E

Project Task1

stablishment of an Advisory Board Gap #1


2 Select Water Utility Partners Gap #1




Gap #2



Gap #2

8 Develop Sensor Placement Guidance Gap #4

uantify Flow and Water Quality Dynamics Through Real‐Time Modeling

Gap #3

9 Develop Toolkit Gap #510 Test and Evaluate Toolkit Gap #511 Validate Toolkit Gap #5


Initial Award

Year One

21

22


Decision Making

Task 1: Establishment of Advisory Board

Advisory Board• DHS Water Sector (John Laws)

• USEPA NHSRC (Robert Janke)

• USEPA Water Security Division (Katie Umberg)

• Kentucky Division of Water (Terry Humphries)

• American Water Company (Nick Santillo)

• (3) Large Water Utilities

– NKYWD (Amy Kramer)

– Louisville (Jim Brammell)

– Denver (Arnold Stasser)

• (1) Medium Sized Water Utility – Nicholasville KY (Tom Calkins)

• (1) Small Water Utility – Paris KY (Kevin Crump)

• Sandia Laboratory (William Hart)

• ATSDR (Morris Maslia)

• University of Louisville (Jim Graham)

• KY/TN AWWA (Mike Bethurem)

• ERDC‐CERL‐IL (Mark Ginsberg) 23

Advisory Board Mission• Facilitate interaction with the water sector

• Provide input on project

– Goals

–Objectives

– Deliverables

24

Deliverable 1.1 Advisory Board Mission Statement (100%)

Advisory Board Guidance• Review project:

– Goals

– Objectives

• Provide feedback and suggestions:– Project tasks

– Project deliverables

25

Deliverable 1.2 Advisory Board Guidance Document (50%)

Project TasksTask #

1 E

Project Task1







Gap #2



Gap #2



Gap #3



Initial Award

Year One

26

27


Decision Making

Task 2: Select Utility Partners

Utility Partners• Large Water Utilities

– NKYWD (Amy Kramer) – 28.2 MGD*– Louisville (Jim Brammell)– Denver (Arnold Strasser)

• Medium Sized Water Utilities– Nicholasville KY (Tom Calkins) – 4.4 MGD– Richmond KY (Danny Pearson) – 6.3 MGD

• Small Water Utility– Paris KY (Kevin Crump) – 1.8 MGD– Berea KY (Donald Blackburn) – 2.9 MGD* Average Daily Demand

28Deliverable 2. Memoranda of Understanding (100%)

Utility Partners

29

Paris

Berea

RichmondNicholasville

NKYWD

LWC

http://upload.wikimedia.org/wikipedia/commons/7/72/Map_of_Kentucky_highlighting_Fayette_County.svg

Paris Kentucky System

Total # of Customers 4875Average Daily Demand (MGD) 1.81# of Tanks 3Capacity (MG) 2.45# of High Service Pumps 2Pipe Diam. Ranges (inches) 1 to 18Total Length of Pipe (mi) 112.3# of Valves 465# of Hydrants 312 30

Nicholasville Kentucky System

31

Northern Kentucky Water District• Serving approximately 300,000 people in Campbell and Kenton counties and portions of Boone, Grant and Pendleton counties

• Over 300 Square Miles of Total Service Area • 1,250 Miles of Main • Three (3) Water Treatment Plants with a Capacity of 64 Million Gallons Per Day (MGD)

• Sixteen (16) Distribution Pump Stations • Twenty (20) Water Storage Tanks

3232

NKWD Statistics

Junctions 11722Pipes 13553Tanks 20Pumps 43PRVs 33Pressure Zones 8Production (MGD) 28

elevation

33

Project TasksTask #

1 E

Project Task1




Gap #1

Gap #1

3 Survey and Evaluate SCADA Systems4 Build Laboratory Scale Hydraulic Model

of Selected Water Distribution System5 Develop Graphical Flow Distribution

Model

Gap #1Gap #2

Gap #2



Gap #2

8 Develop Sensor


9 Develop Toolkit


Gap #3

Gap #4Gap #5



Initial Award

Year One

34

35


Decision Making

Task 5: Graphical Flow Distribution Model

Ben AlbrittonDoug Wood, KYIPIPE LLC

Dr. Lindell Ormsbee University of Kentucky


36

Task 5: Graphical Flow Distribution Model

Use readily available network data from the Kentucky Infrastructure Authority website to build network model of selected system.

Provide total system demand and distribute demands

Input pump station discharge or pump head and visualize flow distribution

GIS Datasets

Graphical Flow Distribution Model

KYPIPE, EPANET, etc

37Deliverable 5.1 Graphical Flow Model and User’s Manual (25%)

KIA Database

38

39

Water LinesWater TanksWater MetersPump StationsEtc

40

Water Line Data:SizeMaterialDate of InstallationSpatial Coordinates

Export to KYIPIPE

Functionality

Build graphical model of distribution system

Add tanks, reservoirs, and pump grades

Distribute total demand amongst nodes

Graphical flow distribution

41

Project TasksTask #

1 E

Project Task1




Gap #1

Gap #1



Model

Gap #1Gap #2

Gap #2



Gap #2

8 Develop Sensor


9 Develop Toolkit


Gap #3

Gap #4Gap #5



Initial Award

Year One

42

43


Decision Making

Task 6: Model Calibration

Joe GoodinDr. Lindell OrmsbeeUniversity of Kentucky

Task 6: Develop and Calibrate a Hydraulic and Water Quality Model

• Create a hydraulic and water quality model of the Nicholasville System, and a hydraulic model of the Paris system which will be used to…– assist with the development of a graphical flow distribution model to help provide utilities of any size with guidance in support of operational decision

– assist with the development of a laboratory scale model of the water distribution system

– provide a context for general recommendations and guidelines for model calibration

44

Data Collection/ Estimates• GIS Data Kentucky Infrastructure Authority Website – Pipe Length, Diameters, and Materials – Tank, Pump, Valve, and Hydrant Data Collected Directly from Utility

• Elevation Data Digital Elevation Model (DEM)• Demand Data Water Meter Data• Demand Pattern Telemetry Data/Field Observations

• Pipe Roughness (Hazen Williams Coefficient)– Estimated using suggested values– Calibrated values using previous Fire Flow Tests

45

Hydraulic Calibration• KYPIPE Software

• C‐Factor Testing– 10 Sampling Locations

• Fire Flow Testing– 10 Sampling Locations

Factors used to Determine Test Sites– Age of Pipes– Material– Diameter– Amount of Flow– Expected Head loss– Accessibility

46

47C‐Factor Testing Site Fire Flow Testing SiteStorage Tank Water Treatment Plant District Master Meter

Water Quality Calibration

• KYPIPE/EPANET Software

• Perform a Tracer Study– Proposed Tracer: Fluoride

– WTP currently Fluoridates Water

• Sampling Approach– Grab Sampling

• Field analysis

• Laboratory analysis48

Water Quality Calibration• Sampling Sites

– Determined using Hydraulic/Water Quality Model

– Hydrants

– Taps

• Testing

– Field Test using Fluoride Colorimeter

– Laboratory Testing at UK

Deliverable 6.1 Utility Partner Data Report (25%)

Deliverable 6.2 Sampling QAPP (50%)

Deliverable 6.3 Hydraulic Calibration Report (Nicholasville and Paris Systems)

Deliverable 6.4 Water Quality Calibration Report (Nicholasville System)49

Project TasksTask #

1 E

Project Task1







Gap #2



Gap #2

8 Develop Sensor


9 Develop Toolkit


Gap #3

Gap #4Gap #5



Initial Award

Year One

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51


Decision Making

Task 4: Physical Model DevelopmentMatt Jolly

Dr. Scott Yost University of Kentucky

Water Distribution System Physical Model

• Task 4:– Design and construct a representative model of Nicholasville’s water distribution system

– Instrument the model with tracer injector and data collection equipment

– Calibrate the lab model with the KYPIPE model and perform water quality tests


• Current Objectives:– Skeletonize and scale Nicholasville model

– Build infrastructure for physical model

– Equip model with sensing equipment for data acquisition


• Skeletonized water distribution system (Nicholasville):– Reduced system to water mains 10” and larger

– Skeletonized model contains pumping station and 3 water towers

– Represented water usage data with 10 demand nodes


• Pipe supporting system:– Uses 2 levels of aluminum trays (60 ft length)

– Supported by angle brackets and ½” steel threaded rods

– Includes platforms for 3 water towers


• Physical model design:– Based on skeletonized model– Scaled by residence time and turbulent flow– Contains 37 pipes (1”, 1½”, and 2”), 10 demand nodes

– Uses 3 elevated tanks (110 gallons each)– Circulates water using a 900 gallon reservoir with a 3 HP end‐suction pump

– Will be equipped to inject tracer for contaminant simulation


• Data acquisition system:– 6 Electrical conductivity sensors to determine tracer concentrations

– 15 Ultrasonic in‐line flow meters

– 19 Pressure sensors

– 4 Tank level sensors

– All sensing equipment will connect to one data acquisition system


• Next Steps:

– Install sensing equipment and data acquisition system

–Calibrate physical model to prototype

–Perform water quality tests with calibrated model

Deliverable 4.1 Physical Model Design Report (50%)Deliverable 4.2 Physical Model Construction Report (50%)Deliverable 4.3 Physical Model Analysis Report 66

Project TasksTask #

1 E

Project Task1




Gap #1

Gap #1



Model

Gap #1Gap #2

Gap #2



8 Develop Sensor Placement Guidance


Gap #2

Gap #3

Gap #49 Develop Toolkit Gap #510 Test and Evaluate Toolkit Gap #511 Validate Toolkit Gap #5


Initial Award

Year One

67

68


Decision Making

Task 3: Survey and Evaluate SCADA Systems

Dr. Robert Reed, University of Missouri

Task 3: Survey & Evaluate SCADA Systems• Implementation of real‐time model requires SCADA interface. This will first require:– Determining:

• types of water SCADA systems currently used• number of operational SCADA systems employed • location & number of SCADA systems utilizing a water distribution model to supplement operations

• how SCADA systems are utilized for operational, security and/or incident response management

– Identifying the spectrum of current mgt use of SCADA data that will contribute to the toolkit development

– Assessing the extent of SCADA data use in mgt decisions

69

Survey Sections• General SCADA system information

– What is being used• SCADA functions

– Security Monitoring– Ambient Air/Water Quality Monitoring– Equipment Management– Data Management– Process Control– Alarm Handling

• “Survey taker” general information– Helps to ID size of system and who is taking survey

70

Schedule & Process of Survey• Survey for utilities & survey for SCADA suppliers

• Survey tool & questions under development

• Project team, AWWA & EPA to review

• If too many questions, will conduct 2 surveys for each target group

• Advertising & posting survey on‐line

• 1st survey on‐line by Aug 2011

• Survey report due EOY 20117171

Survey Preparations• Drafted questions

• Consulted technical survey specialist

–Performed internal validity check

–Modified questions

• Transcribed questions into survey tool (Qualtrics)

72

Survey Deployment• Discussions with Matt Umberg (U.S. EPA)

– EPA is doing a targeted systems survey–Discussed compliments with our general survey

• Institutional Review Board (IRB) Process–Required to protect responders’ identification– Training for all of our researchers– Submission of IRB application–Awaiting “expedited” approval

• Survey out for review upon receipt of IRB approval

73

Report Preparation• Facility visits planned to help with report preparations– Less than 10,000 customers

• Marceline, MO – 2200• Boonville, MO ‐ 8200

– Between 10,000 and 100,000 customers• Marshall, MO – 12,400• St. Charles, MO – 61,000

– Greater than 100,000 customers• Columbia, MO – 100,700• St. Louis, MO – 1,049,600

• Anticipate differences in use based on size of facility

74

Deliverable 3.1 Utility SurveyDeliverable 3.2 WDS SCADA Assessment Report

Project TasksTask #

1 E

Project Task1




Gap #1

Gap #1



Model

Gap #1Gap #2

Gap #2



Gap #2

8 Develop Sensor


9 Develop Toolkit


Gap #3

Gap #4Gap #5



Initial Award

Year One

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76


Decision Making

Task 7: Quantify Flow and Water Quality Dynamics Through Real-Time Modeling

Dr. Jim UberDr. Dominic Boccelli

University of Cincinnati

Task 7: High‐Level Objectives

• Understand the nature of flow and water quality changes in distribution systems, and how accurately they can be predicted using network models

• Build upon/extend previous work on developing and testing real‐time network models

• Rigorously test models and assumptions using field data 77

Why These Objectives?

• The ability to predict network hydraulics and water quality dynamics in real‐time is fundamental to robust event detection and emergency response

• Consider event detection, as an example…

78

Current Use of typical water quality sensors (e.g., free chlorine) filtered by an event detection filter

SCADA

Quality Sensor (Cl, Sp. Cond., TOC, …)

Event Detection filter

Supervisory Control AndData Acquisition

79

Current Event Detection Concepts (e.g. linear filter)

0 500 1000 1500 2000 25000

20

40Node 181 - Contaminant OP

0 500 1000 1500 2000 2500-5

0

5Chlorine signal and estimator

0 500 1000 1500 2000 25000

5

10Absolute Estimation Error

0 500 1000 1500 2000 25000

0.5

1Detection State, S(λ)

80

Traditional off‐line network models could produce a more sophisticated event detection filter

SCADA

Hydraulic Sensor (P, Q, Pump Status, …)Quality Sensor (Cl, Sp. Cond., TOC, …)

Hyd. Model WQ Model

– +EstimatedChlorine

MeasuredChlorine

Prediction Error


Off‐Line Network Model

81

Off Line Network Model‐Based Event Detection Filter

0 500 1000 1500 2000 25000

20


0 500 1000 1500 2000 25000

2


0 500 1000 1500 2000 25000

2


0 500 1000 1500 2000 25000

0.5


82

Real‐time network models could improve prediction accuracy and reduce false‐positives, producing a robust

event detector

SCADA Real‐Time Hyd. Model

Real‐Time WQ Model

– +


Real‐Time Network Model

MeasuredChlorine

(Improved)EstimatedChlorine

Prediction Error

Updated Demands /Real Control Actions

Hydraulic Sensor (P, Q, Pump Status, …)Quality Sensor (Cl, Sp. Cond., TOC, …) 83

On Line Network Model‐Based Event Detection Filter

0 500 1000 1500 2000 25000

20


0 500 1000 1500 2000 25000

2


0 500 1000 1500 2000 25000

2


0 500 1000 1500 2000 25000

0.5


84

Message• It makes more sense to alarm based on deviations from our best prediction of what should be occurring, versus deviations from a black box predictor that doesn’t know our distribution system

• Promise of Lower rate of false positives and false negatives

85

Real‐Time Modeling Overview

86

87

Current Real‐Time Data Capabilities

MonitoringStation

MIU

ISAs

Flow Meter

Flow Metering

Pressure Monitoring

Pump/Valve Status

Quality Monitoring

SCADADatabase

Current: Data and Model Separation

88

Operations Planning

SCADA

Steady state design scenarios

Calibration using limited data

Little relevance to operations

Future: Data and Model Integration

89

Operations Planning

EPANET-RTX{real-time extension}

SCADA

SCADA

EPANET-RTX{real-time extension}

Model SparsemeasurementsSystem‐wide

Q, H, C

EPANET‐RTX converts sparse SCADA data to a complete estimate of the system state

Pump statusPlant ProductionClearwell/tank levelsOther pressures/flows

90

RTX Compute Cycle• Gather new data from SCADA Historian

• Interpret the measurements

• Allocate Demands

• Run a Simulation

• Save Results

93

Epanet-RTX

WDSSensor Network

SCADADB

Results DB

model.inpdevices.xml

config.rtx

Demand Allocator

HydraulicEngine

94

Research Plan• Previous Research

– EPANET‐RTX algorithms and code base for hydraulic modeling

– Configured and Installed at Northern Kentucky Water District

• New Project Milestones

– Develop and execute plan to improve / calibrate network model

– Implement new adaptive calibration algorithms within EPANET‐RTX

– Extend Epanet‐RTX to incorporate water quality prediction

– Conduct model verification field studies to quantitatively assess hydraulic and water quality prediction accuracy

• Datalogging pressure transducers

• Tracer study to measure flow dynamics by specific conductance

– Analyze data and summarize measured statistical water quality and hydraulic variability

– Assess ability to predict both hydraulics and water quality in real‐time

Deliverable 7.1 Water Quality/Flow Dynamics Data AnalysisDeliverable 7.2 Water Quality/Flow Dynamics Sensitivity Report

96

Project TasksTask #

1 E

Project Task1







Gap #2



Gap #2



Gap #3



Initial Award

Year One

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Decision Making

Task 8: Develop Sensor Placement Guidance

Amanda LothesDr. Sebastian BrysonUniversity of Kentucky

Task 8a

• The program TEVA‐SPOT has been developed to help provide the optimal locations for network sensors to protect public health in a contamination event, but it is unlikely that smaller utilities will use TEVA‐SPOT for such applications.

• Develop general guidance for small/medium sized utilities– Water Quality Sensor Placement

99

Task 8b

• TEVA‐SPOT determines sensor placement for water quality sensor purposes, but it does not consider objectives related to placement of hydraulic sensors in order to obtain useful information about network hydraulic dynamics.

• Develop general guidance for large utilities– Water Quality Sensor Placement

• General operations• Real time model support

– Hydraulic Sensor Placement• Real time model support

100

Task 8a – Water Quality Sensor Placement Guidance

• Develop guidance for water quality sensor placement for small and medium sized utilities.– Sensors

• Water quality– Operations

• General• Allow smaller utilities access to guidance for sensor placement without the necessity of running TEVA‐SPOT for their system

101

TEVA‐SPOT• The Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Toolkit

• A software to support the design process of contamination warning systems.

102TEVA‐SPOT home screen with Execution Control Panel

TEVA‐SPOT

• Developed by the US Environmental Protection Agency, Sandia National Laboratories, Argonne National Laboratory, and the University of Cincinnati.

• The Sensor Placement algorithm solves an optimization problem of placing a certain number of sensors in the distribution network to optimize a stated objective, usually subject to several constraints.

103

Sensor Placement Screen Shot from TEVA‐SPOT

How TEVA‐SPOT Works• Three Main Software Modules: simulate incident, assess consequences, and optimize sensor placement

104

EPANET

• Developed by EPA's Water Supply and Water Resources Division

• Software that models water distribution systems and performs extended‐period simulation of the hydraulic and water quality behavior of a pipe network.

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EPANET

• EPANET tracks the flow of water in each pipe, the pressure at each node, the height of the water in each tank, and the concentration of a chemical species throughout the network during a simulation period.

106

System Selection

• Test systems are obtained by using waterlines from the KIA database of systems smaller than 3 MGD and turning them into functional models.

• These models will represent the three spatial configurations: loop, branch, and grid.

107

Grid Systems• Grid systems are characterized by transmission lines that run from the source to distribution mains in the center of the system to arterial mains on the outer sections.

• Arterial and distribution mains interconnect at roadway intersections and other regular intervals.

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Loop Systems• A looped system consists of connected pipe loops throughout the area to be served

• Loop systems have distribution mains that loop around the system and are characterized by arterial lines as the system moves inward.

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Branch Systems

• Branch systems are characterized by smaller pipes branch off larger pipes throughout the service area.

• This type of system is most frequently used in rural areas.

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Sensor Placement Guidance Development Process

• Model small and medium distribution systems in three different spatial configurations: loop, grid, and branch.

• Observe changes in flow rate, headloss, and pressure as system is skeletonized and system characteristics are altered

• Run TEVA‐SPOT on systems and observe patterns of sensor placement and correlate with system characteristics.

• Develop general rules for sensor placement.

• Make predictions for sensor placement on new systems. • Run TEVA‐SPOT on systems to confirm that guidance recommends similar sensor placement design.

111

Decision Charts

112

Progress

• Test systems have been studied in EPANET and flow characteristics and physical characteristics of systems have been measured at sensor locations chosen by TEVA‐SPOT

• 10 models have been built from the waterlines in the KIA database of systems serving less than 3 MGD. These models will be used as test systems to develop general sensor placement rules.

113

Task 8b – Hydraulic Sensor Placement Guidance

• Develop guidance for sensor placement for large utilities.– Sensors

• Hydraulic• Water quality

–Operations• Real Time Model Support

114

Deliverable 8.1b Real Time Sensor Placement Guidance Report (5%)

Sensor Placement Guidance Development Process

• Model large distribution systems in different spatial configurations.• Observe nodes with largest fluctuations in flow rate and pressure as system is skeletonized and system characteristics are altered

• Develop algorithm in a sensor placement program to perform sensor designs that support real time operations.

• Run program on systems and observe patterns of sensor placement and correlate with system characteristics to develop general rules for sensor placement.

• Make predictions for sensor placement on new systems. • Run program on systems to confirm that guidance recommends similar sensor placement design.

115

Hydraulic Sensor Placement Guidance

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System Model

N Scenario ResultsDatabase

Operational Scenarios

Database of System Models

EPANET/RTXDemand Allocator

Model Time Series

System Model

Validation Time Series Final Sensor Set

Sensor Placement Optimization Model

Sensor Type (Q, P, L)

Sensor Location

Sensor Time series

Model Validation Time Series

Residual Time Series

N‐1

1

Project TasksTask #

1 E

Project Task1





of Selected Water Distribution System

Gap #1

Gap #1Gap #2



Gap #2

Gap #2




Gap #3



Initial Award

Year One

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Decision Making

Task 9: Operational Toolkit

Andrew ErnestWestern Kentucky University

Tasks 9: Operational ToolkitWater Distribution System Operational Toolkit

Rule Based Decision Support Tool (RBDST)

Graphical Flow Distribution Model

Guidance Rules GIS Datasets

KYPIPE

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Deliverable 9.1 Template of the Operational Toolkit (5%)Deliverable 9.2 Beta Version of Operational Toolkit

Semantic Knowledge Development

Task 3 Utility Survey

Task 4 Physical Model

Task 6 Model

Calibration

Task 7 Flow

Dynamics

Task 8 Sensor

Placement

If………………… Then……………………. Rules

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Rules‐Based Decision Support ToolFACTS (e.g. sensor status)

Rule Database: Collection of unsorted /unlinkedRules (e.g. IF_____ THEN______)

HYPOTHESIS(e.g. incursion event)

Inference Engine: Software that examines database so as to find

links between FACTS and HYPOTHESES

Knowledge Development(e.g. interview experts)

Forward Chaining

BackwardChaining

What facts arerequired to support a given hypothesis?

What hypothesisis supported by the given facts?

What are the rulesbeing used to supporta final conclusion (e.g. required facts or hypothesis?)

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Project TasksTask #

1 E

Project Task1





of Selected Water Distribution System

Gap #1

Gap #1Gap #2



Gap #2

Gap #2




Gap #3



Initial Award

Year One

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Decision Making

Task 10&11: Technology Deployment

Lindell OrmsbeeUniversity of Kentucky

Andrew ErnestWestern Kentucky University

Task 10&11: Technology DeploymentWorkshop ImplementationDemonstration

Feedback Revisions Feedback Revisions

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Deliverable 10.1 Toolkit Evaluation ReportDeliverable 11.1 Toolkit Validation ReportDeliverable 11.2 Advisory Board Toolkit Assessment ReportDeliverable 11.3 Final Operational ToolkitDeliverable 12.1 Final Report

Final Comments and Questions

125

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