optimizing booster chlorination in small municipalities… · dr. manuel j. rodriguez optimizing...
TRANSCRIPT
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