Smart Urban Water Systems...Mar 01, 2020 · ARMAX model: 0.2140 1 0.1822 2 0.1018 3 0.1634 4...

Smart Urban Water Systems

- some preliminary thoughts

Zhiguo Yuan AM, FTSE, IWA Distinguished Fellow

ARC Australian Laureate Fellow

Director, Advanced Water Management Centre

27 Feb 2020

Why do I feel passionate about it?

CRICOS code 00025BPresentation Title | Date 2

My Bachelor (1985)Sensors & instrumentation

My PhD (1992)Automation (AI)

Beijing University of Aeronautics and Astronautics

My career to dateUrban water management

Ghent University & The University of Queensland

Smart urban water systems

Knowledge-based fault diagnosis in dynamic systems (Yuan, Z. PhD thesis, 1992)

Smart Urban Water Systems


IoT sensors

Network communication

…

Sensing

Local control

Transmissions

…

Real-timecontrol

Learned status

Recommended strategies…

Data storage

Data analytics

Optimization

…

Computing power

Machine learning

Artificial intelligence

…

Supervisorycontrol

Planners

Managers

Operators

Customers

Operational/managing/planning decisions

Smart water system vs. ICA


• Smart water system encompasses ICA

Smart water

system

ICA: Instrumentation, control and automation

Smart water system vs. ICA


• Smart water system encompasses ICA

Smart water

system

ICA: Instrumentation, control and automation

but expanding towards

• higher levels of duties- More integrative - More supervisory- Decision support (operational,

management and planning, behaviour change)

• higher level of intelligence- IoT sensors- Big data- Machine learning & artificial intelligence

Technology push

• Internet of Things

• Low-cost sensors and low-cost, yet powerful

microchips

• Large data storage capabilities

• Fast computing e.g. iCloud computing

• Advanced data analytics

- machine learning

- artificial intelligence

6Presentation Title | Date CRICOS code 00025B

Driving forces for smart urban water

Demand pull

• Population growth and continued

urbanisation imposes more pressure to

urban water systems

• Climate change causes more variability

• Aging infrastructure

• Customer expectation

• Integrated urban water management is

becoming more important

Where are the opportunities (non-exhaustive)


Treatment

Domestic

use

Storm

water

Treatment

Treatment

Treatment

Surface

Water

Seawater

Ground

water

Industrial

use

Other

uses

SewersReceiving

waterWWTP

Distribution

TreatmentRecycled

water

CSO/SSO

Smart metering to

understand and change

consumer behavior

Integrated wastewater management

1. Sewer, WWTP and receiving water

2. Central and decentralized systems

Other possibilities

1. Business process automation

2. Interactions with customers

Example 1: water source optimization – the orange council case study

CRICOS code 00025B 8

Rainfall/runoff

Evaporation

Users

Shearing Shed bore

Stormwater schemes (SW)

Spring Creek Dam

Macquarie River

Showground bore

Bore 5 Batch pond

Suma Park Dam

Holding pond

Blackmans SW

BrooklandsSW

Somerset SW

MitchellCargo

SW

Escort SW

Env. flows

Env. flows

Env. flows

Env. flows

Losses Losses

Env. flows

Water Treatment

Plant

See Fig. 53 for details of

model downstream of Suma Park

Dam

Several water sources

(natural catchments, groundwater,

stormwater harvesting and

Macquarie River)

Several constraints

(environmental flows,

source withdrawal

limits, water restrictions)

Several objectives

(minimisation of costs, spill,

greenhouse gas emissions and

maximisation of environmental flows)

Presentation Title | Date

© CRC for Water Sensitive Cities

Dr Lisa Blinco, Uni Adelaide

Example 2: water production based on demand prediction


OUTIN

L

Controller /Switch

Set-point

Production flowDistribution

flowReservoir0%

20%

40%

60%

80%

100%

0

2000

4000

6000

8000

10000

22-Apr 00:00 22-Apr 06:00 22-Apr 12:00 22-Apr 18:00 23-Apr 00:00

m3 /

h

System #4, level based control

Distribution Production Reservoir level [%]

OUTIN

L

Prediction- and control algorithm

Set-point

Production flowDistribution

flowReservoir 0%

20%

40%

60%

80%

100%

0

2000

4000

6000

8000

10000

12-Apr 00:00 12-Apr 06:00 12-Apr 12:00 12-Apr 18:00 13-Apr 00:00

m3 /

h

System #4, predictive control

Distribution Production Reservoir level [%]

Flow prediction

model learnt from

historical data

Bakker et al. (2013)

Example 3: tackling non-revenue water


Example from Unitywater (QLD) potable water distribution network using TaKaDu platform (Israel):

• Real-time flow data versus Network-based prediction flow model = leak detection shortly after burst & detection of hidden

(underground) leaks

• $16 million AUD per annum in savings from early detection of leaks

• 6.5 billion litres of non-revenue water loss prevented in 2017 due to enabling of rapid, fit-for-purpose and localised response

Example 4: water consumption pattern analysis – changing customer behavior


One-off exceptions

accounted for 31% of

all water use

© CRC for Water Sensitive Cities

Prof Rachel Cardell-Oliver, UWA

14,000 smart meters installed

City of Kalgoorlie-Boulder

Example 5: online optimization of pump operations

Odour control at inlet of a WWTP

13 pump stations; 26 km

ADWF = 14.3 ML/day

Mg(OH)2 ⇌ Mg2+ + 2OH-

H2S ⇌ HS- + H+

Magnesium dosing

PS2 WWTP

PS3

PS4

PS6

PS7

PS8

PS5

3000 m

Φ0.4

m

162 m

Φ0.325m

218 m

Φ0.6m

100 m

Φ0.3

m

259 m

Φ0.45m

356 m

Φ0.2

25m

1441 m

Φ0.45m

498 m

Φ0.3

75m

1484 m

Φ0.45m

225 m

Φ0.225m

787 m

Φ0.3

25m

477 m

Φ0.3

75m

873 m

Φ0.525m

372 m

Φ0.1

m

544 m

Φ0.525mCP1 CP2 CP3 CP4 CP5 CP6

PS1

1822 m

Φ0.5

25m

TH5

TH3

88 m

Φ0.2

25m

TH2

57 m

Φ0.1

m

TH1

537 m

Φ0.1

5m

C30

1215 m

Φ0.3

3m

C31

7 m

Φ0.1

5m

2037 m

Φ0.525m

470 m

Φ0.525m

1100 m

Φ0.525m

200 m

Φ0.525m

200 m

Φ0.525m

400 m

Φ0.525m

Treatment

plant

Odour control at inlet of a WWTP

• Hybrid system – linear model predictive control algorithm not directly applicable

• Large search spaces – efficient optimization algorithms required

PS2 WWTP

PS3

PS4

PS6

PS7

PS8

PS5

30

00

m

Φ0

.4m

162 m

Φ0.325m

218 m

Φ0.6m

10

0 m

Φ0

.3m

259 m

Φ0.45m

35

6 m

Φ0

.22

5m

1441 m

Φ0.45m

49

8 m

Φ0

.37

5m

1484 m

Φ0.45m

225 m

Φ0.225m

78

7 m

Φ0

.32

5m

47

7 m

Φ0

.37

5m

873 m

Φ0.525m

37

2 m

Φ0

.1m

544 m

Φ0.525mCP1 CP2 CP3 CP4 CP5 CP6

PS1

18

22

m

Φ0

.52

5m

TH5

TH3

88

m

Φ0

.22

5m

TH2

57

m

Φ0

.1m

TH1

53

7 m

Φ0

.15

m

C30

12

15

m

Φ0

.33

m

C31

7 m

Φ0

.15

m

2037 m

Φ0.525m

470 m

Φ0.525m

1100 m

Φ0.525m

200 m

Φ0.525m

200 m

Φ0.525m

400 m

Φ0.525m

• Controlled case: 15% of time below target

• Uncontrolled case: 38% of time below target

Example 5: online optimization of pump operations

Machine learning


• Block-box (data driven)

⎼ Statistical modelling

⎼ Artificial neural network

⎼ Large amount of data

⎼ Identifiability issue

( ) ( ) ( ) ( ) ( ) ( )1 1 1A z y t B z u t C z e t− − −= +

ARMAX model:

Processu(t) y(t)

Machine learning







ARMAX model:

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( )

0.2140 1 0.1822 2 0.1018 3 0.1634 4

0.1014 5 0.0869 6 0.0543 7 1.5261

ˆ ˆ ˆ ˆ

1

0

ˆ

.7946 2 0.0285 3 0.

ˆ ˆ

292

ˆ

1 4 ( )

y t y t y t y t y t

y t y t y t u t

u t u t u t e t

= − + − + − + − +

− + − + − + − +

− − − + − +

Li et al. (2019)

Machine learning


Machine learning







• Grey-box

⎼ Data supplement existing knowledge

⎼ Model-supported data analysis or data-enabled model identification

⎼ Process knowledge required

⎼ Smaller amount of data needed

Prior knowledge is important

We should not ask the machine to invent the Bernoulli equation!

Modelling of methane product ion in gravity sewers

A Simplified M odel for M ethane Product ion in Gravity Sewer

Methane product ion rate in a gravity sewer is proport ional to the area of biofilm that is in contact

with wastewater. For a given wastewater flow (Q), pipe diameter (D) and slope (S), the biofilm

area can be theoret ically calculated as follows (Akgiray, 2004).

Figure 1: Schemat ic of water flow in a sewer pipe

✓=3⇡

2

vuuuut 1−

vuuut 1−

vuut

⇡Dn

D83 S1

2

(1)

Where Q is flow rate in m3/ s, n is the pipe frict ion factor, D is pipe diameter in m, and S is the

slope.

The above relat ionship can be represented by a simplified expression as follows:

✓= k ·Q↵·D β ·Sγ (2)

Theoret ical ✓values were est imated using Eqn 1 for a series of pipe size with diameter ranging from

100 mm to 3000 mm, and pipe slope ranging from 0.0005 to 0.02. The correlat ion between the ✓

values thus calculated and those est imated using the simplified equat ion (Eqn 2) after determining

the parameters through regression is presented in Figure 2.

Area of biofilm can thus be calculated using the simplified expression as follows:

Abf = ✓·D

2·L = k ·Q

↵·D β ·Sγ ·

D

2· L (3)

Abf = k ·Q↵·D β ·Sγ · L (4)

Where L is length of pipe in m.

Methane product ion rate can then be est imated as

r CH 4= k

0

·Abf = k ·Q↵·D β ·Sγ · L (5)

Where r CH 4is the methane product ion rate in kg CH4/ day.

Considering 1 km length of pipe and the temperature of 20oC as standards, methane product ion

rate can be expressed as follows:

r CH 4 ,20 = k ·Q↵·D β ·Sγ (6)

February 1, 2016 Page 1

Machine learning







• Grey-box

⎼ Data supplement existing knowledge

⎼ Model-supported data analysis or data-enabled model identification

⎼ Process knowledge required

⎼ Smaller amount of data needed

𝑘𝐶𝐻4,𝑇 = 𝑎1𝑇𝑏1 + 𝑎2𝑄

𝑏2 + 𝑎3𝐷𝑏3 + 𝑎4𝑠

𝑏4

𝑘𝐶𝐻4,𝑇 = 𝑘 𝑇 𝑄𝛼𝐷𝛽𝑠𝛾

D: diameters: slopeQ: flow rateT: temperature

Segmented efforts need to be united


WHAT UTILITIES WANT TO ACHIEVE?

(DEMAND PULL)

WHAT DO TECHNOLOGIES OFFER?

(TECHNOLOGY PUSH)

SHARED

OBJECTIVES

JOINT

EFFORT

• Urban water system will get smarter!!

• While isolated case studies have been done, there is a lack of a systematic

framework

• Collaborative efforts from utilities, hardware suppliers, software suppliers,

and researchers needed

• Multi-disciplinary research is required

Concluding remarks


Acknowledgements


Smart urban water system workshop

Brisbane, Nov 26, 2018

Smart Urban Water Systems...Mar 01, 2020 · ARMAX model: 0.2140 1 0.1822 2 0.1018 3 0.1634 4...

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