Optimizing booster chlorination in small municipalities… · Dr. Manuel J. Rodriguez Optimizing...

byNilufar Islam

PhD Candidate

Supervisors:Dr. Rehan Sadiq

Dr. Manuel J. Rodriguez

Optimizing booster chlorination in small municipalities: a risk-cost trade-off analysis

Presentation outline

•

• Objectives

• Methodology

• Results Explanation

• Case study & Comparison

• Future Scope

• Conclusions

Background & Motivation

2

3

0 410.2

USEPA Surface Water Treatment Rule

WHO 1997

0.7

Disinfectant by-products (DBPs): Cancer,reproductive problems… Taste & Odour:

Customer rejection

0.5

Australian - Odor threshold

0.6

Booster stations to balance DBPs and FRC

Canadian WDN: 0.04 to 4 mg/L (need management)

Background & MotivationRegulations-Free residual chlorine (FRC)

4

Residual scale

4

a)

4 mg/L

0.6 mg/L

0.5 mg/L

0.001 mg/L

Background & Motivation

b)

ChlorinationGeneral

Traditional With booster chlorination

5

Booster chlorinationAdding additional chlorine in the WDN to increase residual chlorine

Microbial, chemical (DBPs), and

aesthetic water quality

Less possible risk of cancer from

DBPs

Less amount of chlorine application-

less costs

Careful selection of dosage &

locations for smaller municipalities

Effects

Background & Motivation

DBPsFree Cl2Pathogen

6

Challenges with smaller municipalities:

Example: Two-thirds of provincial systems in BC are small

& rural communities with frequent boil-water advisories

Not adequate water treatment

Chlorination can be the only treatment

Non-availability of high qualified staff

Optim

ization&

Decision m

akingBackground & Motivation

• Combined with other parameter such as

TTHM

• Less locating studies for booster stations

• Optimization was based on residual chlorine only

• Cost calculation is difficult

7

Background & Motivation

Locating booster stations with an index • Represents regulatory violation

• Combines complex data, e. g., temporal data

Proposed approach

Limitations in previous studies

• Cost for health compromise

Presentation outline

•

•

• Methodology

• Results Explanation

• Case study & Comparison

• Future Scope

• Conclusions

Background & Motivation

8

Objectives

9

Objectives

To locate booster stations for chlorination in smaller water

distribution networks which can:

• ensure adequate water quality,

• with less risk due to health compromise, and

• at the cost of less resources ($$) and technical

personnel

Presentation outline

•

•

•

• Results Explanation

• Case study & Comparison

• Future Scope

• Conclusions

Background & Motivation

10

ObjectivesMethodology

11

Methodology

Define kinetics

EPANET MSX FRC

TTHM

Modified CCME WQI Optimization

Preliminary booster location detection

Quadraticoptimization

CHCl3, BDCM, DBCM, &

CHBr3

Unit risk-cancer

Hazard index-non-cancer

$/ DALY averted

Trade-off analysis

Start

Fini

sh

12

Methodology

Define kinetics

EPANET MSX FRC

TTHM

Modified CCME WQI Optimization

Preliminary booster location detection

Quadraticoptimization

CHCl3, BDCM, DBCM, &

CHBr3

Unit risk-cancer

Hazard index-non-cancer

$/ DALY averted

Trade-off analysis

Start

Fini

sh

1

_( )( ...... )

i i

i

Q ModifiedCCME WQIMax fMax Q Q

13

Methodology

Define kinetics

EPANET MSX FRC

TTHM

Modified CCME WQI Optimization

Preliminary booster location detection

Quadraticoptimization

CHCl3, BDCM, DBCM, &

CHBr3

Unit risk-cancer

Hazard index-non-cancer

$/ DALY averted

Trade-off analysis

Start

Fini

sh

14

Methodology

1 booster 2 boosters 3 boosters …. ….. ….. N boosters

Water quality

Unit risk (Cancer) &

Hazard index (non-cancer

$/DALY averted (CEA)

Cost effectiveness analysis (CEA): DALY (Disability-adjusted life

year)

Water quality: Modified CCME WQI (Islam et al. 2013)

Preliminary booster locations: MCLP optimization

Presentation outline

•

•

•

•

• Case study & Comparison

• Future Scope

• Conclusions

Background & Motivation

15

ObjectivesMethodology

Results Explanation

16

Results Explanation

Water reservoir

Proposed booster stations

Nodes for result observation

EPANET Programmers’

toolkit

28 nodes

First order chlorine decay• Kb: Bulk-coefficient

=0.0331/hrFirst order TTHM decay• F: Linear proportionateconstant=0.651

MCLP Optimization-

MATLABModified

CCME WQI (Islam et al. 2013)

15 18

20 21

16

28

24 27

17

Results Explanation

4.E-065.E-065.E-066.E-066.E-067.E-067.E-068.E-068.E-06

Ris

k In

dex

(RI)

RI- Node 15RI- Node 18RI- Node 20RI- Node 21

18

Results Explanation

505560657075808590

00.020.040.060.08

0.10.12

WQ

I

Cos

t ($/

DA

LY

ave

rted

)x

1000

00

Cost ($/DALY averted)

WQI (Node 15)

4042444648505254565860

00.010.020.030.040.050.060.070.08

WQ

I

Cos

t ($/

DA

LY

ave

rted

)x 10

0000

Cost ($/DALY averted)

WQI (Node 18)

505560657075808590

00.010.020.030.040.050.060.070.080.09

WQ

I

Cos

t ($/

DA

LY

ave

rted

) x 10

0000

Cost ($/DALY averted)

WQI (Node 20)

Node 15 Node 18

Node 20

5052545658606264

00.010.020.030.040.050.060.070.08

WQ

I

Cos

t ($/

DA

LY

ave

rted

) x 10

0000

Cost ($/DALY averted)

WQI (Node 21)

Node 21

50

52

54

56

58

60

62

64

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 booster 1 booster 2 boosters 3 boosters 4 boosters

WQ

I

Cos

t ($/

DA

LY a

vert

ed)

x 10

0000

Cost ($/DALY averted)

WQI (Node 21)Node 21 Effect

Presentation outline

•

•

•

•

•

• Future Scope

• Conclusions

Background & Motivation

19

ObjectivesMethodology

Results Explanation

Case study & Comparison

16 Booster stations

20

Study area

2,598 water mains

EPANET 2.0

5 reservoirs

20 water tanks

21

298 nodes Proposed booster

stations 5 Dosage used

0.8mg/L

City of Kelowna Case Study

Nodes for result observation

22

City of Kelowna Case Study

23

City of Kelowna Case Study

0.00E+005.00E-061.00E-051.50E-052.00E-052.50E-05

3.00E-05

3.50E-05R

isk

Inde

x (R

I) ET180J-6090J-6265J-6365J-6443J-6483J-6486

24

City of Kelowna Case Study

0

10

20

30

40

50

60

70

80

90

100

0

20

40

60

80

100

120

WQ

I

Cos

t ($/

DA

LY a

vert

ed)

Cost- Node ET180 WQI- Node ET180

Cost-Node J-6090 WQI-Node J-6090

Cost- Node J-6265 WQI- Node J-6265

Cost- Node J-6365 WQI- Node J-6365

Cost- Node J-6443 WQI- Node J-6443

Cost- Node J-6483 WQI-Node J-6483

Cost- Node J-6486 WQI- Node J-6486

25

City of Kelowna Case Study

Improved

Unchanged

0

50

100

150

200

250

300

0 to 1booster

to 2boosters

2 to 3boosters

3 to 4boosters

4 to 5boosters

Num

ber

of n

odes

Improved

Degraded

Unchanged

1

108 65

125

151

282

17 0

281

14

39

245

14 0

284

2626

1

2

3

4

Proposed booster station Current booster stations

The proposed scheme shows similar results

Saves time, and resources ($$)

City of Kelowna Case Study

Presentation outline

•

•

•

•

•

•

• Conclusions

Background & Motivation

27

ObjectivesMethodology

Results Explanation

Case study & Comparison

Future Scope

28

Future Scope- IntrusionOptimization: microbial & chemical risk trade-off

1. Identify intrusion points

3. Predict Nodal Effects

4. Optimization

2. Intrusion

29

Future Scope- IntrusionIdentify Intrusion points

Diameter

Resistivity, soil pH, Moisture content

etc.

Nodal importance

Structural failure

Risk of Intrusion

Pipe

cha

ract

eris

tics

data

Length

Installation yr

Soil

data

Soil Corrosively

Population

Land useCity

info

Nodal Pressure

30

Future Scope- IntrusionIdentify Intrusion points

EPANET

31

Identify Intrusion PointsApply E. Coli concentration

Estimate nodal effects

E. Coli TTHM

TTHM species

TCM DBCM BDCM Bromoform

Chemical risk (CR)

QMRA

Optimization: MOGA

Min (CR)

Min(QMRA)

ArcGIS 10

EPANET-Pressure

Future Scope- Intrusion

Presentation outline

•

•

•

•

•

•

•

Background & Motivation

32

ObjectivesMethodology

Results Explanation

Case study & Comparison

Future Scope

Conclusions

Conclusions and future scope

An optimization scheme has been proposed to locate booster locations

Firstly, the scheme considered an index using regulatory thresholds for

TTHM and FRC

The index can account microbial, chemical and aesthetic water quality

Finally, the booster stations have been selected using a trade-off

analysis with hazard Index, unit risk, and cost effectiveness analysis

The model has been implemented on a part of city of Kelowna water

main system

The model can be very useful for smaller communities

Contaminant intrusions can be included in this model in future for

microbial-chemical trade-off analysis

33

34

Acknowledgement

National Science and Engineering Research Council

RES'EAU-WaterNET

35

Questions ?Thank you

“If there is magic on this planet, it is contained in water.”Loren Eiseley

36

CWQI or CCME Ranges from 0 to 100

F1=Scope

• % of failed variables

F2= Frequency

• % of regulatory violation

F3= Amplitude

• Amount of violation2 2 2

1 2 3100 ( )1.732

F F FCCME WQI

Modification in CCME WQI

b=0.2mg/lM N

1

Chlorine, mg/lc= 0.8mg/l

a b c dM N

1

Chlorine, mg/lModified CCME

Simpler-one variable for F2, and F3

More logical

Advantages- modified CCME-WQI

3737

a b c dM N

1

Chlorine, mg/l

b, Lower regulatory limit= 0.2mg/l

c, Upper regulatory limit= 0.8 mg/l

0.1 0.2 0.8 1M N

1

Chlorine, mg/l

Time (hr) Cl2 (mg/l) Fuzzyexcursion (FE)

49 0.18 0.250 0.17 0.3. .. .

240 0.16 0.4

0.18

0.2

nsfe=

V=

# _ intFE

All po s

0.005 0.005nsfe

nsfe

100ModifiedCCME WQI V

Example modified CCME-WQI

38

0102030405060708090

1 2 3 4 5

Mod

ified

CC

ME

WQ

I

Number of booster station

J-1140J-6070J-6080J-6370J-6447

Recommendation: 3-4 booster stations should be used

City of Kelowna Case Study