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Leakage Forensic Analysis for Water Distribution
Systems: A Fuzzy-Based Methodology
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General
Objectives
Leakage Overview
Tools and Techniques
Framework 1: Evaluating Leakage Potential
Case study 1: Leakage Potential
Framework 2: Leakage Detection and Diagnosis
Case Study 2: Leakage Detection and Diagnosis
Conclusions
2
Presentation Outline
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General: Typical Water Distribution System
Haestad et al. 2003
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Water
Quantity Quality
Continuity
Objective of WDS
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Unintentional or accidental loss of water from WDS
Constitutes a major portion of non-revenue water
(NRW)
An important component of standard water balance
Leakage Overview
6
What is leakage potential?
Likelihood of leakage occurrence
An effective 100% LP of a WDS means that the production
volume and the LP are same in that WDS.
What is leakage?
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Burst
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Burst
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Bac
kgroundleakage
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Leakage
Bac
kgroundleakage
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City (%) of Production
Volume
City (%) of Production
Volume
Bangkok 38 Kuala Lumpur 45
Colombo 56 Manila 63
Delhi 54 Philadelphia (real Loss) 26
Dhaka 40 Phnom Penh 22
Ho Chi Minh 39 Seoul 26Hong Kong 26 Shanghai 18
Jakarta 51 Tashkent 28
Karachi 30 Ulaanbaatar 38
Kathmandu 38 Makkah (real Loss) 32
Non-revenue Water around the Globe
11
Continent (%) of Production
Volume
Continent (%) of Production
Volume
Africa 39 Latin America
and Caribbean
42
Asia 42 North America 15
Non-revenue Water= Leakage loss + management loss
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Implication of Leakage
Financial losses Consumer problems
Intrusion of contaminants and health risks
Damage to infrastructures
Increased loading on sewers
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Time (days)
Flow(m3/d)
A
Connection Burst: 400 m3
L R
25
75
16 days
Time (days)
Flow
(m3/d)
A
Property Burst: 1150 m3
L R
25
75
46 days
Importance of Quick Detection
Time (days)
Flow
(m3/d)
A
Mains Burst: 80 m3
L R25
751.1days
A - awareness
L - locationR - repair
Lambert , 1994
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Objectives
To evaluate leakage potential (LP) in WDS
To detect and diagnose leakage in WDS
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Tools and Techniques
EPANET programmers toolkits- USEPA developed codes for
WDS hydraulic and quality simulation
Fuzzy Set Theory
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2/9/2004 16
Linguistic variables are used to represent qualities
spanning a particular spectrum Temperature : {Freezing, Cool, Warm, Hot}
It is 30% Cool and 70% Warm
50 70 90 1103010
Temp. (F)
Freezing Cool Warm Hot
0
1
0.7
0.3
Fuzzy Set Theory
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Framework 1: Evaluating Leakage Potential
Two parts
Part1: Rule-based fuzzy inference modeling
Part 2: Pressure adjusted leakage potential
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Hierarchical Leakage Potential ModelLeakage Potential in
Pipe
X1,01
Pipe
Attribute,
X1,3
3
PipeMaterial
Pipe
Diameter
Loading
TrafficFlow
CoverDepth
Workmanship,
X5,1
2
Pipe
Placement
Beddingand
Backfill
Compaction
Pipe
Workmanship,
X2,5
3
Net.Instru.
Workmanship
Joints,
Meters,
SCs,
X2,3
3
Traffic
Impact,X1,1
3
Physical,
X3,1
2
External,X1,1
2
Ground
Condition
Impact,X2,1
3
Demand,
X3,3
3
Residential
Comercial
Industrial
X1,53
X1,14 X2,1
4 X3,14
X7,14
X8,14 X12,3
4 X13,34 X14,3
4 X14,24 X15,2
4 X16,24
SoilType
GWT
able
Fluctuation
Temperature
Fluctuation
X4,24 X5,2
4 X6,21
Numberof
Service
Connections
Numberof
Water
Meters
Numberof
Joints
Ge
neration1
Xi,jk
ParentChild
Generation
Generation2
Generation3
Generation4
Age,
X4,1
2
PipeAge
Net.
Instrument
age
X1,43
X2,43
Pressure,
X2,1
2
System
Pressure
Headloss
X1,23
X2,23
X9,24 X10,2
4 X11,24
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0 . 0 0
0 . 2 0
0 . 4 0
0 . 6 0
0 . 8 0
1 . 0 0
1 . 2 0
1 . 4 0
0 . 0 0 0 . 1 0 0 . 2 0 0 . 3 0 0 . 4 0 0 . 5 0 0 . 6 0 0 . 7 0 0 . 8 0 0 . 9 0 1 . 0 0 1 . 1 0 1 . 2 0
R a t io o f P r e s s u r e s P 1 /P o
R
atio
o
fL
eakag
e
R
ates
1/Lo
N 1 = 0 . 5 0
N 1 = 1 . 0 0
N 1 = 1 . 1 5
N 1 = 1 . 5 0
N 1 = 2 . 5 0
L1/Lo = (P1/P0)N1
N1= Pressure Exponent or
Emitter coefficient ( EPANET)
Pressure Adjustment
19
(Farly and Trow, 2003 )
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Case Study 1: Evaluating Leakage Potential
Bangkok: Latitude: 13
0
45 N and Longitude: 100
0
30 E
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Study District Metering Area (DMA)
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Data Value Data Value
Area coverage 2.5 km2 Average daily pressure (Sept. 2004) 12.56 m
Population served 3570 pers. Non revenue water, May 2004 37.3%
No. of metered properties 820 (0) Maximum pipe diameter 300 mm
Total Pipe length 17.5 km Pressure Exponent 1.16No. of valves 19 Major Pipe Materials PVC and AC
DMA-0144, Bangkoknoi at a Glance
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40
45
50
55
60
65
70
0 10 20 30 40 50 60 70 80 90
Leakagepotential(
%)
Pressure ( m)
40
45
50
55
60
65
70
75
80
0 10 20 30 40 50 60 70 80 90Leakagepotential(%)
Age (year)
System Pressure =45m
System Pressyre =12.56m
LP with varying operating
system pressure
LP with age for two
different operating
system pressures
Calculated leakage potential for DMA-0144 =48%Model Results
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R, ND, RL, D,
L, K, etc
Model and
Simulation
Engine
P,
F,
Q,
A
Parameters Simulation Output
R,
ND,RL,
D, L, K, etc
Model and
Simulation
Engine
Parameters Simulation Output
P, F,Q, A
(a)
(b)
Y
Y' Y
Y1 Y2
0.2
0.6
1.0
0.0
0.8
0.4 0.2
0.6
1.0
0.0
0.8
0.4
1.0
1.0
1.0
1.0
1.0
1.01.0
1.0
1.0
1.0
1.0
1.0(x)
(x)
(x)
(x)
L H
A
A A
A
Y
Framework 2: Leakage Detection and Diagnosis
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Simulation outputWDS Failure
(b)
YY' Y
0.2
0.6
1.0
0.0
0.8
0.40.2
0.6
1.0
0.0
0.8
0.4(x)
(x)
Simulation output
P, F
1-r
T
P, F,Q, A
m
L H
R=1-r
0.2
0.6
1.0
0.0
0.8
0.4(x)
r
O
O
(a)f
f
f
P, F,Q, AP, F,Q, A
Y1 Y2
Y1
Y2
Y
Y' YY1
Y2
0.2
0.6
1.0
0.0
0.8
0.4(x)
Monitored Data
Leakage Detection Framework (Contd)
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(b)
Simulation output
P, F
1-r
T
P, F,Q, A
m
L H
R=1-r
0.2
0.6
1.0
0.0
0.8
0.4(x)
r
O
O
Y1
Y2Y
Y' YY1
Y2
0.2
0.6
1.0
0.0
0.8
0.4(x)
Monitored Data
Index of Leakage Propensity (ILP)
= (monitored flowflow most likely flow) / (extreme flow value flow most likely flow)
Leakage Detection Framework (Contd)
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1
2 3 4
5 6 7
8 9 10 11
12 13 14 15 16
17 18 19 20
21 22 23 24 25
26 27 28 29
30 3132
33
34
35
36 37
38 39
40
3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22
23 24 25
26 27
1 2
No. of Pipes: 40
Number of Nodes: 27
Total Pipe Length: 19.5 km
Diameter: 200-700 mm
Base Demand
25.00
50.00
75.00
100.00
LPS
Diameter
302.00
404.00
506.00
608.00
mm
Case Study 2: Leakage Detection and Diagnosis
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1
2 3 4
5 6 7
8 9 10 11
12 13 14 15 16
17 18 19 20
21 22 23 24 25
26 27 28 29
30 3132
33
34
35
36 37
38 39
40
3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22
23 24 25
26 27
1 2
Leakage Data Preparation
Adding leakage demand at node 20 (Emitter coefficient: 1.5)
Simulating WDS with leakage demand
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Index of Leakage Propensity
IndexofLeakagePropensity(ILP)
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1
2 3 4
5 6 7
8 9 10 11
12 13 14 15 16
17 18 19 20
21 22 23 24 25
26 27 28 29
30 3132
33
34
35
36 37
38 39
40
3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22
23 24 25
26 27
1 2
Time
(hr)
Leaky node no. in order Leaky pipe no. in order
(1) (2) (3) (4) (1) (2) (3) (4)
2 20 25 15 19 29 25 34 37
Most Probable Leakage Node Identification
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Conclusions
A novel methodology has been developed for evaluating leakage
potential in the distribution system
A novel methodology has been developed for leakage detection
and diagnosis
Model will help utility managers for a ALC policy, rehabilitation
policy and consequently better WDS management practices
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Acknowledgements
21:06:36 32
Financial Support: NSERCSPG Project
Data: ATACO ( Bangkok) Ltd and MWA , Bangkok
Images (some): WRP (Pty) Ltd , South Africa
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Thanks for your attention
Q & A?
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