The use of artificial intelligence techniques to predict the flow in sewers and at CSOs.

Adrian Saul and Will ShepherdPennine Water Group, University of Sheffield

a.j.saul@sheffield.ac.uk

Water Industry Forum 18th May 2011

Contents

• Introduction• Background to data collection in 2

catchments• Application of AI to data and

diagnostic tools• Summary comment

Introduction• In 2005 YW set up a Strategic Partnership

with the University of Sheffield• As part of this partnership two strategic

research catchments were established• Long term monitoring of rainfall, sewer

flow and CSO flow depth• This presentation highlights the

application of Artificial Intelligence techniques that use this data to enhance understanding of system performance

Catchment 1• Population ~ 17,000. Main town + large village + 2

small villages.• Town on steep hill, villages in valley bottom.• Village flows pumped to WwTW in town.

Flow monitoring

• Ultrasonic Doppler velocity sensor.

• Ultrasonic downward looking depth sensor.

• Pressure sensor for surcharged depths and provides redundancy.

• Recorded at 2 minute resolution.

Depth monitoring

• CSOs – two types of ultrasonic depth monitor, one battery powered, one mains.– 5 or 15 minute resolution.

• Tanks use flow meters without velocity sensor.– 2 minute resolution.

Rain gauges

•Tipping bucket rain gauges, 0.2 mm depth per tip.•Tips aggregated into 2 minute rainfall intensities.

Catchment 2• Population ~

15,000. Villages / Suburbs upstream of city.

• Mainly in steep valley with some flatter areas.

• Several off-line storage tanks

Domestic Gully Sensors

Post Mounted Hub

GPRS Internet

Web Based Data Interaction

Web Server /Data Backup

Reports

Direct Alarms

Alarms

Rainfall Radar Data

EquipmentService

Simple, five level Sewer monitors

Business Planning and Development

Sewer Ops Management

Field Engineers -Installation,

Service,Etc.

Depth/Flow Logger

Low Cost, Event Based Sensors

StatusConfiguration

Location

Low cost rain gauge

Direct Reports

Future Vision

Data Data ––Knowledge Knowledge --

ActionAction

Data in real time Data in real time –– rainfall, flow, pollutionrainfall, flow, pollution

Fast simulation of system performanceFast simulation of system performance

Predictive modelling Predictive modelling –– artificial intelligenceartificial intelligence

Proactive management, operations and Proactive management, operations and interventionsinterventions

Sensor optimisation

Daily patterns

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00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Time

Flow

(m3 /s

)

Mon Tues Weds Thurs Fri Sat Sun

CSO level prediction

Data inputData at 5 to 15 minute time steps

Rainfall Hawkeye depth Sewer Flow

Neural NetworkData used to train model (more data better model)

Output data Prediction of depth 15 minutes into future

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000250

300

350

400

450

500

550

Monitoring time steps (5 mins interval)

Cha

mbe

r wat

er le

vel (

mm

)

Model prediction output sample from Carleton Rd Skepton CSO

Prediected valueActaully value

Chamber weir height

Chamber weir

Prediction model output sample

Blockage Detection

0

2

4

6

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10

12

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21/05/08 26/05/08 31/05/08 05/06/08 10/06/08 15/06/08 20/06/08 25/06/08Time

Flow

(l/s

)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Velo

city

(m/s

) & D

epth

(m)

Flow (l/s)Depth (m)Velocity (m/s)

A typical data set showing the relationship between rainfall and depth but with system failures Diagnostic fuzzy logic techniques are used to identify irregularperformance in CSO’s with and without screensFailure has now been linked to operational performance and strategies for proactive maintenance identified

Summary CommentAI techniques have been applied to sewer system data and it has been feasible to:1 Predict sewer flow and CSO depth using measured rainfall2 To develop diagnostic tools to identify unexpected changes in system performance – changes in DWF, rapid blockage or gradual build-up of water depth.3 To develop diagnostic tools to identify differences in measured and predicted wet weather performance4 To link the performance of CSO chambers and CSO chambers with screens to maintenance frequency5 To develop strategies for proactive maintenance

Thankyou