Optimizing booster chlorination in small
municipalities: a risk-cost trade-off analysis
by
Nilufar Islam
PhD Candidate
Supervisors:
Dr. Rehan Sadiq
Dr. Manuel J. Rodriguez
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
2
Background & Motivation
Regulations-Free residual chlorine (FRC)
USEPA Surface Water Treatment Rule
 Disinfectant by-products
(DBPs): Cancer,reproductive
problems…
 Taste & Odour:
Customer rejection
1
0
0.2
0.5 0.6
0.7
4
WHO 1997
Australian - Odor threshold
Canadian WDN: 0.04 to 4 mg/L (need management)
Booster stations to balance DBPs and FRC
3
Background & Motivation
Traditional
a)
With booster chlorination
b)
General
Chlorination
Residual scale
4 mg/L
0.6 mg/L
0.5 mg/L
0.001 mg/L
4
Background & Motivation
Booster chlorination
Adding additional chlorine in the WDN to increase residual chlorine
Effects
 Microbial, chemical (DBPs), and
aesthetic water quality
 Less possible risk of cancer from
DBPs
Free Cl2
Pathogen
DBPs
 Less amount of chlorine applicationless costs
 Careful selection of dosage &
locations for smaller municipalities
5
Background & Motivation
Challenges with smaller municipalities:
& rural communities with frequent boil-water advisories
 Not adequate water treatment
 Chlorination can be the only treatment
 Non-availability of high qualified staff
Optimization
& Decision making
Example: Two-thirds of provincial systems in BC are small
6
Background & Motivation
Limitations in previous studies
•
Less locating studies for booster stations
•
Optimization was based on residual chlorine only
•
Cost calculation is difficult
Proposed approach
Locating booster
stations with an
index
•
Combined with other parameter such as
TTHM
•
Represents regulatory violation
•
Combines complex data, e. g., temporal data
•
Cost for health compromise
7
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
8
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
9
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
10
Methodology
Start
Define kinetics
EPANET MSX
FRC
Modified
CCME WQI
Optimization
TTHM
Preliminary booster
location detection
Quadratic
optimization
Unit risk-cancer
Hazard indexnon-cancer
$/ DALY
averted
Trade-off
analysis
Finish
CHCl3, BDCM,
DBCM, &
CHBr3
11
Methodology
Start
Qi  ModifiedCCME _ WQI i
Max( f ) 
Max(Q1......Qi )
Define kinetics
EPANET MSX
FRC
Modified
CCME WQI
Optimization
TTHM
Preliminary booster
location detection
Quadratic
optimization
Unit risk-cancer
Hazard indexnon-cancer
$/ DALY
averted
Trade-off
analysis
Finish
CHCl3, BDCM,
DBCM, &
CHBr3
12
Methodology
Start
Define kinetics
EPANET MSX
FRC
Modified
CCME WQI
Optimization
TTHM
Preliminary booster
location detection
Quadratic
optimization
Unit risk-cancer
Hazard indexnon-cancer
$/ DALY
averted
Trade-off
analysis
Finish
CHCl3, BDCM,
DBCM, &
CHBr3
13
Methodology
$/DALY averted
(CEA)
Unit risk (Cancer)
&
Hazard index (non-cancer
Water quality
1 booster
2 boosters 3 boosters ….
…..
….. N boosters
Water quality: Modified CCME WQI (Islam et al. 2013)
Preliminary booster locations: MCLP optimization
Cost effectiveness analysis (CEA): DALY (Disability-adjusted life
year)
14
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
15
Results Explanation
28
Water reservoir
28 nodes
15
20
16
18
21
First order chlorine decay
• Kb: Bulk-coefficient
=0.0331/hr
First order TTHM decay
• F: Linear proportionate
constant=0.651
27
24
EPANET
Programmers’
toolkit
Modified
CCME WQI (Islam
et al. 2013)
MCLP
OptimizationMATLAB
Proposed booster stations
Nodes for result observation
16
Results Explanation
Risk Index (RI)
8.E-06
8.E-06
7.E-06
7.E-06
6.E-06
6.E-06
5.E-06
5.E-06
4.E-06
RI- Node 15
RI- Node 18
RI- Node 20
RI- Node 21
17
0.05
0.04
Node 15
62
60
58
Cost ($/DALY averted)
Cost ($/DALY averted)
WQI (Node 15)
WQI (Node 18)
Node 18
56
54
0.01
0
0 booster
1 booster
90
85
80
75
70
65
60
55
50
Cost ($/DALY averted)
x 100000
0.02
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
64
2 boosters
Node 21 Effect
Cost ($/DALY averted)
WQI (Node 20)
Node 20
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
3 boosters
4 boosters
64
62
60
58
56
54
52
50
52
WQI
0.03
60
58
56
54
52
50
48
46
44
42
40
WQI
0.06
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
WQI
0.07
Cost ($/DALY averted)x 100000
90
85
80
75
70
65
60
55
50
WQI
Cost ($/DALY averted)x 100000
0.08
WQI
x 100000
0.12
0.1
0.08
0.06
0.04
0.02
0
Cost ($/DALY averted)
Cost ($/DALY averted)
x 100000
Results Explanation
50
Cost ($/DALY averted)
Cost ($/DALY averted)
WQI (Node 21)
WQI21
(Node 21)
Node
18
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
19
Study area
EPANET 2.0
2,598 water mains
5 reservoirs
16 Booster stations
20 water tanks
20
City of Kelowna Case Study
Nodes for result observation
 298 nodes
 Proposed booster
stations 5
 Dosage used
0.8mg/L
21
City of Kelowna Case Study
22
City of Kelowna Case Study
3.50E-05
Risk Index (RI)
3.00E-05
2.50E-05
2.00E-05
1.50E-05
1.00E-05
5.00E-06
0.00E+00
ET180
J-6090
J-6265
J-6365
J-6443
J-6483
J-6486
23
City of Kelowna Case Study
120
100
90
100
70
80
60
60
50
WQI
Cost ($/DALY averted)
80
40
40
30
20
20
10
0
0
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
24
City of Kelowna Case Study
282
284
281
Improved
245
Degraded
Number of nodes
300
Unchanged
250
125
200
150
108
65
100
39
1
50
15
17
0
14
14
0
Unchanged
Improved
0
0 to 1
booster
1 to 2
boosters
2 to 3
boosters
3 to 4
boosters
4 to 5
boosters
25
City of Kelowna Case Study
4
2
3
1
Proposed booster station
Current booster stations
 The proposed scheme shows similar results
 Saves time, and resources ($$)
26
26
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
27
Future Scope- Intrusion
Optimization: microbial & chemical risk trade-off
1. Identify
intrusion points
3. Predict Nodal
Effects
2. Intrusion
4. Optimization
28
Future Scope- Intrusion
City info Soil data Pipe characteristics data
Identify Intrusion points
Diameter
Length
Structural failure
Installation yr
Resistivity, soil pH,
Moisture content
etc.
Risk of Intrusion
Soil Corrosively
Population
Nodal importance
Land use
Nodal Pressure
29
Future Scope- Intrusion
Identify Intrusion points
EPANET
30
Future Scope- Intrusion
Identify Intrusion Points
Apply E. Coli
concentration
ArcGIS 10
EPANET-Pressure
Estimate nodal effects
E. Coli
TTHM
TTHM species
QMRA
TCM
DBCM
BDCM
Bromoform
Chemical risk (CR)
Optimization: MOGA
Min(QMRA)
Min (CR)
31
Presentation outline
• Background & Motivation
• Objectives
• Methodology
• Results Explanation
• Case study & Comparison
• Future Scope
• Conclusions
32
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
Acknowledgement
National Science and Engineering
Research Council
RES'EAU-WaterNET
34
“If there is magic on this
planet, it is contained in
water.”
Loren Eiseley
Q
uestionsyou
?
Thank
35
Modification in CCME WQI
CWQI or CCME
Ranges from 0 to 100
 F1=Scope
1
• % of failed variables
 F2= Frequency
• % of regulatory violation
M b=0.2mg/l c= 0.8mg/l N
Chlorine, mg/l
 F3= Amplitude
• Amount of violation
CCME  WQI  100  (
F12  F22  F32
1.732
)
1
Advantages- modified CCME-WQI
Simpler-one variable for F2, and F3
More logical
M a
b
c d N
Chlorine, mg/l
Modified CCME
36
Example modified CCME-WQI
1
1
0.2
Ma b
c d N
Chlorine, mg/l
b, Lower regulatory limit= 0.2mg/l
M
0.1
0.2
0.18
 FE
N
Time (hr)
Cl2 (mg/l)
Fuzzy
excursion (FE)
49
0.18
0.2
50
0.17
0.3
.
.
.
.
240
0.16
c, Upper regulatory limit= 0.8 mg/l
nsfe=
0.8 1
Chlorine, mg/l
# All _ po int s
nsfe
V=
0.005nsfe  0.005
ModifiedCCME  WQI  100  V
0.4
37
Modified CCME WQI
City of Kelowna Case Study
90
80
70
60
50
40
30
20
10
0
J-1140
J-6070
J-6080
J-6370
J-6447
1
2
3
4
Number of booster station
5
Recommendation: 3-4 booster stations should be
used
38