Fuzzy based Leakage Forensic Analysis

7/29/2019 Fuzzy based Leakage Forensic Analysis

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University of British Columbia | Okanagan1

Leakage Forensic Analysis for Water Distribution

Systems: A Fuzzy-Based Methodology


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University of British Columbia | Okanagan

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?


7/33University of British Columbia | Okanagan

Burst


8/33University of British Columbia | OkanaganPhoto: Courtesy R Liemberger

Burst



Bac

kgroundleakage



Leakage

Bac

kgroundleakage



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



12

Implication of Leakage

Financial losses Consumer problems

Intrusion of contaminants and health risks

Damage to infrastructures

Increased loading on sewers



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



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

15



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



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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University of British Columbia | Okanagan 30

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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Documents

Fuzzy based Leakage Forensic Analysis