Modeling of distribution system to optimize detection of water quality events using multi-site approach.

1. New Multi-Site
Approach

2. “Multi-Site” Approach
• Use upstream data to
“account” for variability at a
downstream “target” site
– event = episode of significant,
unaccountable variability
originating between sites
• Upstream sites provide
– incoming WQ conditions
– more operational parameters
• System-wide coverage by
cascading along circuits from
WTP
= Tank
= Pump St.
= WTP
17
7
14
1
9
2
5
8
3
16
15
11
64
13 12
Circuit 3
Circuit 1
Circuit 2
Circuit 4
10

3. COND (mS/cm) TEMP (deg. F)
1-hour time steps (220 days, August to March)
CL2 (mg/l)
PH
CL2
PH
COND
TEMP
Upstream vs. downstream WQ
• Trends similar but not identical – because of target site
operations, measurement errors, unknown causes
upstream
flow target

4. COND (mS/cm) TEMP (deg. F)
1-hour time steps (220 days, August to March)
CL2 (mg/l)
PH
CL2
PH
COND
TEMP
Upstream vs. downstream
upstream
flow downstream

5. Multi-Site Accounting
• Accounting performed by empirical “process
models”
– prediction error = variability due to unknown causes
– statistically large error = event
– models = curve fits by artificial neural networks (ANN)
Inputs
predicted
DCL2
PH
measured
DCL2
yes
keep
monitoring
COND
empirical
process
model
CL2upstream WQ
upstream
operations
target
operations
Target Site
Outputs
prediction
error too
BIG?
no
notification

6. 4-site example
• BPS B is “target” site
• 1 year 4-min data – 1st 10 months for
development, last 2 months for test
BPS
A
TANK
A
unmonitored
flows
Q, PSUC, PDIS,
COND, CL2, TEMP
LVL,
COND,
CL2
TANK
B
BPS
B
Q, PSUC, PDIS,
COND, CL2, TEMP
LVL,
COND,
CL2

7. BPS B COND model results
4-minute observations
measured predicted
COND (mS/cm) Training Data
N: 76,148
R2: 0.847
RMSE: 72 mS/cm
Test Data
N: 17,296
R2: 0.893
RMSE: 69 mS/cm

8. BPS B CL2 Process Model – training data
CL2 (mg/l)
4-minute observations
measured predicted
Test Data
N: 11,715
R2: 0.912
RMSE: 0.085 mg/l
Training Data
N: 41,894
R2: 0.837
RMSE: 0.085 mg/l
nitrification?
drop outs?

9. Test data CL2, COND, PH 20-min. D’s
• measured and predicted D’s (left axes)
• prediction errors and alarm limits (right axes).
– alarm limits = error that occurs 0.1% of time (1 / 2.8 days)
CL2
COND
PH
error & limits
meas. & pred. D’s
2 days of 4-minute observations

10. streaming
data
star
plot
streaming
graphs
nearest
neighbor stats
COND
tracking
PH
tracking
CL2
tracking
scalars
tracking
nearest neighbor
distributions
example

11. 4-D Tracking
• Makes multi-parameter process data more
understandable to operators
• Helps identify incipient process problems
such as nitrification

12. Conclusions – Multi-Site Approach
• Accounts for all measured causes of WQ
variability to reduce false positives & negatives
– ARMADA demo available
• Process models know cause-effects between
operational & WQ parameters
– potential use to improve distribution system WQ
• Reasons other than EDS to monitor distribution
system
– control processes to improve delivered WQ
– detect problems - low CL2, nitrification, line integrity,
DBPs