Urban Water Security Research Alliance

A Real-time Event Detection System

for Wastewater Source Control

a collaborative project between CSIRO and

Griffith University

Roger O'Halloran, CSIRO Australia

SEQ water supply security

•Water grid

•Desalination

•Potable recycled water (PRW) scheme

SEQ water quality research alliance:

•Supports PRW scheme

•Qld state govt, CSIRO, UQ, Griffith Uni

Background

7-barrier SEQ PRW scheme

Inter-barrier

monitoring points

CC monitoring

points within

a barrier

Our project:

•Water quality information management

Project team:

•Prof Huijun Zhao (Griffith Uni)

•Dr Roger O‟Halloran (CSIRO)

Objectives:

•Early warning system

•Treatment plant protection

•Treated water quality

Project Overview

Stage 1

•Review existing online sensing

technologies

Stage 2

•Build online WQ monitoring system

•Develop event detection maths

•Demonstrate event detection system

Stage 3

•Real-time prototype

Project Objectives

System Configuration

• Simple and robust

• Low power and small (if portable)

Analytical aspects

• Consumes little or no reagent

• Little on-going calibration or

maintenance

• Stable and reliable sensors

Requirements for online WQ analysis

WQ analysis system must

• provide long-term, continuous, real-time

water quality information

• enable operators/managers to manage

potential risks at the earliest possible

barrier/control point

• improve level of control, better safeguard

plant operation

Desired Outcomes for End-users

Typical online water quality

information collection systems

Current online WQ systems

• Perform well in the laboratory

• Few have good field performance

• None satisfactory for raw sewage

Why?

• Most use analytical methods developed

for laboratory use

• Require strictly controlled

measurement conditions

• Difficult or impossible in the field

Current online WQ systems

Traditional analysis…

• Determines amounts of a particular

substance

• Allows decisions about the system

• Does it meet regulatory requirements?

• Can it be discharged?

• Ultimate purpose of analysis is to

characterise the system

• Accurate parameter measurement not

essential, so long as precise system

characterisation is achieved

Research context: 7-barrier scheme

Inter-barrier

monitoring points

CC monitoring

points within

a barrier

Technical Challenges

• As the first control point, Barrier 1 is

critical for the PRW system

• Wastewater sources highly diversified

• Compositions/matrix are complex

• Raw sewage represents most difficult

analytical measurement environment

• Real-time quantitative detection is

practically impossible

Monitoring system for Barrier 1

Utilise sensor signals differently

• Use a number of robust sensors for

different matrix characteristics

• Measure data continuously – get time

change information

• Analyse data collectively

• Correlations between sensor signals

• Look for patterns, trends, events

Develop a real-time event detection

system

Online WQ systems - what can we do?

Our Approach

• Matrix change recognition using

multiple sensors

• Sensors must tolerate sample

matrix, be self-contained

• Each responds to one or more

physical/chemical aspects of matrix

change

• Sensor signals must be acquired

continuously and simultaneously

Our Approach

• Sample characterisation collectively

represented by the sensor signals

• “Blind man‟s buff”!

• Event recognition from time plots

• determine if system is „normal‟ or

„abnormal‟

• A significant event detected by simply

determining changes in sensing signal

• no need to accurately determine

analytical value of each parameter

Our Approach

Event detection maths involves two key

elements:

• Real-time sensor data is clustered as

„normal water‟ matrix and used as the

reference-baseline.

• A set of criteria are used to discriminate

anomalous water matrix changes (events)

from normal water matrix changes

(reference-baseline)

Multivariate Approach

Ala

rm/R

eport

ing

Sam

plin

g

•••

S1

Establishing

Baseline

Identifying

Anomalous

No

S2

Establishing

Baseline

Identifying

Anomalous

No

Sn

Establishing

Baseline

Identifying

Anomalous

No

Yes

Yes

YesCross-checking Abnormality Of

Other Sensors

Colle

ctive

Abno

rmalit

y

Analy

sis

No

No

Yes

Re-establishing

Baseline

Re-establishing

Baseline

Ala

rm/R

eport

ing

Sam

plin

g

•••

S1

Establishing

Baseline

Identifying

Anomalous

No

S2

Establishing

Baseline

Identifying

Anomalous

No

Sn

Establishing

Baseline

Identifying

Anomalous

No

Yes

Yes

YesCross-checking Abnormality Of

Other Sensors

Colle

ctive

Abno

rmalit

y

Analy

sis

No

No

Yes

Re-establishing

Baseline

Re-establishing

Baseline

• A Robust Multivariate Filter used for real-time

establishment of reference-baseline

• Multivariate Statistical Test is used to determine if the

new input data are anomalous relative to the historical

reference-baseline

Univariate Approach

Mimics process used by trained person

1.Reference baseline estimated for each

variable separately using a modified

running median

• eliminates short-term noise

2.Univariate statistical test applied to each

data point

• anomalous relative to the baseline?

Current System Configuration

6 self-contained sensing probes (temp, pH, EC, DO, ORP, turbidity)

Temporal Patterns

Sudden matrix change

Industrial Discharge Event

Event Detection ValidationT

em

p

20.0

20.5

21.0

21.5

22.0

21 Jun 2008 23 Jun 2008

12:006:00 18:00

pH

56

78

21 Jun 2008 23 Jun 2008

12:006:00 18:00

EC

05

10

15

20

21 Jun 2008 23 Jun 2008

12:006:00 18:00

Turb

050

100

150

200

250

21 Jun 2008 23 Jun 2008

12:006:00 18:00

SentinelOne-JUNE2008.Rdata

Black dots: original sensing signals; Green lines: reference-baseline from robust filter; Orange

and red symbols: alerts and event warnings, respectively

Industrial discharge event

Effect of event on effluent quality at Barrier 2

Multivariate – acid discharge eventT

em

p

24.0

24.5

25.0

30 Nov 2008 1 Dec 2008

12:00 12:006:0018:00

pH

23

45

67

89

30 Nov 2008 1 Dec 2008

12:00 12:006:0018:00

EC

0.0

0.5

1.0

1.5

2.0

2.5

3.0

30 Nov 2008 1 Dec 2008

12:00 12:006:0018:00

Turb

050

100

150

30 Nov 2008 1 Dec 2008

12:00 12:006:0018:00

SentinelOne-OCT_DEC2008.Rdata

Univariate – acid discharge event

Storm Overflow Events

Multivariate – storm overflowT

em

p

21.5

22.5

23.5

19 Nov 2008 20 Nov 2008

12:00 12:006:0018:00

pH

5.0

6.0

7.0

8.0

19 Nov 2008 20 Nov 2008

12:00 12:006:0018:00

EC

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

19 Nov 2008 20 Nov 2008

12:00 12:006:0018:00

Turb

050

100

150

200

250

300

19 Nov 2008 20 Nov 2008

12:00 12:006:0018:00

SentinelOne-OCT_DEC2008.Rdata

Univariate – storm overflow

Will enable

• Improved event response

• Safeguard barrier operation

• Better coordination between barriers

• Better source control

• Identify problem discharges

• Characterise plant capacity and

operational protocols/conditions

Real time event detection system

Conclusions

We have devised and experimentally

validated a rapid event detection principle

We have demonstrated the feasibility of

applying the event detection principle for

wastewater source control

The event detection methodology is

effective even in very noisy data

The technique has potential applications in

other water types

Ipswich Water

Research Team

SEQ Alliance and Queensland Government

Acknowledgements