New Multi-Site

Approach

“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

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

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. downstreamupstream

flow downstream

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

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

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 DataN: 17,296

R2: 0.893

RMSE: 69 mS/cm

BPS B CL2 Process Model – training dataCL2 (mg/l)

4-minute observations

measured predicted

Test DataN: 11,715

R2: 0.912

RMSE: 0.085 mg/l

Training DataN: 41,894

R2: 0.837

RMSE: 0.085 mg/lnitrification?

drop outs?

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

CONDPH

error & limits

meas. & pred. D’s

2 days of 4-minute observations

streaming

data

star

plot

streaming

graphs

nearest

neighbor stats

COND

trackingPH

tracking

CL2

tracking

scalars

tracking

nearest neighbor

distributions

example

4-D Tracking

• Makes multi-parameter process data more

understandable to operators

• Helps identify incipient process problems

such as nitrification

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