Smart water solutions by Veolia Public presentation at Hydrogaia in 2014

1. Sensors and modelling
Cornerstones for smart water management

2. Introduction
Forecasting water demand
Managing resources
Managing water distribution networks
Managing waste water networks
Protecting the environment and health
Wrap-up
Agenda
Veolia Environnement Research & Innovation 2
13/05/2014

3. 1
What does SMART water mean?
Veolia Environnement Recherche & Innovation
Solution of
Monitoring providing
Added-value with
Relevant
Technologies
System
Management tools
Assisting operators to
Reduce
Time to decision
Sensors and
Models
Aiding for
Relevant and
Timely decision
S…
M…
A… What Else?
R…
T…
Which concepts are behind the letters?
“SMART” usually synonym of “intelligent”!

4. 1
Some issues that we are facing
and which can be addressed with smart solutions
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Fast growing population
Bathing water
quality
Ressource
management
Sea water infiltration
and pipe corrosion
Seasonal water
demand
Environment
protectionLeaks
Drinking water
contamination
Resource protection
and monitoring

5. Technological challenges…
Veolia Environnement Recherche & Innovation
Water is not oil…costs, costs, costs
The difference with « Smart Grid for Energy »
• Pipe difficult access … (underground)
• Absence of source of energy and communication systems
(water ressources monitoring)
• Standards compliance (with sanitary conformity for water
equipments)
• Complexity and size of infrastructures (cities…)
• Millions of possible contaminants / indicators with
concentrations from mg/L to ng/L

6. Forecasting water demand
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Ressources
Drinking
water
Waste
Water
Needs ?
Population growth, consumption reduction
seasonal demand…

7. Water demand is the key point for short term and long
term management of water services
Our sensors are the water meters over the network and
at the consumption point
Consumption forecast comes from statistical modelling
and/or agent based modelling, taking into account
customers behavior and water use
Current challenges are anticipation of seasonal water
demand (to be investigated in the Matrics project) and
long term forecasts
Forecasting water demand
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60 000
70 000
80 000
90 000
100 000
110 000
120 000
130 000
140 000
150 000
160 000
sept-02 janv-04 mai-05 oct-06 févr-08 juil-09 nov-10 avr-12
Productionmoyennejournalière(m3)
Données Usine
Modèle
Source : VERI
Urban Modelling
(internal project)
Source : Homerider
Business reference:
Long term city planning and modelling activities
have been spinned off
Business reference:
The first smart metering operator in France

8. Managing water resources
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Ressources
Drinking
water
Waste
Water
Water resources are exposed to
• chronic, seasonal or
accidental pollutions
• groundwater depletion
• drought
Tradeoff between
different resources ?

9. Ideally, we should monitor continuously all sensitive water resources
In France, only a dozen of early warning stations monitor raw water upstream
drinking water treatment plant
Actually
Existing monitoring solutions (eg. warning stations ) are often expensive (200 k€ CaPex and OpEx
= 10 % CaPex/year)
European SMEs are developing innovative solutions but these are not necessary well integrated
into the information system and not really compliant with operation constraints (low maintenance…)
Our aim within the Swarm Eureka project is to significantly reduce the total
cost of ownership, especially the operating costs (low maintenance, no
reagent) with two monitoring devices:
A network of autonomous and instrumented
buoys to monitor non specific parameters
(pH, dissolved oxygen, turbidity,
temperature…)
Compact warning station for the analysis of
specific parameters
(pesticide, microcystin…)
Managing water resources
Swarm project (Smart WAter Resource active Management)
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Source : VERI
SWARM (Collaborative project)
Water level
River/Lake bottomSinker

10. More data, more data sources, real time data… structuration and analysis
are more and more difficult!
SWARM contribution:
integration of web based and big data technologies
development of real-time processing algorithms to analyze hundreds of data flows, each
including a dozen of parameters
Managing water ressources
Swarm project (Smart WAter Resource active Management)
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Data analysis
Web
application
Sensors
and Scada data
Web data
and open data
Internal data
bases
Radar and
satellite tiles
Data
integration
Vision of the river basin
integrating real time and
open data to support
resource management

11. RIPOST is a decision support tool dedicated to drinking water treatment
plant operators, developed to anticipate the consequence of a spill
upstream a drinking water treatment plant intake.
Sensors are located upriver in pollution warning station or on a buoy.
Managing water ressources
Ripost (integration of a pollutant transport model)
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o A numerical pollutant spill and transport model has been
implemented. It computes the transfer time and the
concentration gradient evolution along the river and
gives results for decision making such as: transfer time,
maximum concentration and alarms based on
concentration thresholds at the water intake.
o As a decision support tool, it must fulfil operational
needs: ease of use for operators, fast numerical
computations and relevant decision support information
for crisis management and communication with
authorities. Source : VERI, Ripost project
Publications:
Learn more at the upcoming events
• Astee congress in Orleans (June 3rd-6th)
• HIC 2014 in New-York (August 17-21)
Business references:
Ripost has been implemented for SEDIF (Paris suburb) and in the
south-west of France (Garonne and Ariège rivers)
Source : SEDIF

12. Managing water distribution networks
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Ressources
Drinking
water
Waste
Water
Leaks ?
Quality ?
Water and energy footprint?
Source : Le Parisien

13. 4
Managing quality in distribution networks
Sensors KAPTA™ 3000 AC4
Veolia Environnement Recherche & Innovation
• Simultaneous measurement of the most important parameters
(World Health Organization)
• Communicating, autonomous energy and cost-effective sensors
• An infrastructure for collecting and transmitting real-time data via
radio or GSM technology
Parameters of contamination identification
Active chlorine
Conductivity
Temperature
Pressure
Business references:
KAPTA™ 3000 AC4 has been deployed in Shanghai, Nice,
London, in the suburb of Paris, Lyon…
Publications:
Visit http://www.secureau.eu/

14. 4
Managing quality in distribution networks
Smart Sensors KAPTA™ 3000 Solution
Veolia Environnement Recherche & Innovation
FUSION, the graphical
interface providing the
operators with a tool for
decision making to
manage the network

15. Where would you install your sensors?
Optimal location of sensors can be defined, based on an hydraulics and
water quality model and a multi-objective optimization algorithm
Once you have detected a contamination, you have to perform
1) Online Source Identification of Contaminants
2) Risk analysis, identification and evaluation
of impacts (real impacts and perceived ones)
In both case you need an online running model,
which is automatically calibrated to the measured
sensor data.
Acquisition and assimilation of a huge data
stream from quantity and quality sensors
raise challenging problems…
Managing quality in distribution networks
Don’t use sensors without models!
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Publications:
Visit http://www.secureau.eu/
Follow : http://www.smart-onlinewdn.eu/
Source : Veolia Eau

16. Managing waste water networks
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Ressources
Drinking
water
Waste
Water
Asset Management
Odor Control
…

17. Waste water networks are more difficult to monitor and
operate than water distribution networks.
Hydraulic sensors are already availabe
but for sulfur in waste water, we are still looking for
sensors with a low total cost of ownership, as simple and
robust as a gas detector.
Waste water models, including sulfur and organic mater
modelling, are not ready for on-line use.
 These two important aspects will be addressed
in our new collaborative project: Matrics
Managing waste water networks
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Source : Veolia Eau
Gescira software
Source : Homerider
«VEOLIA by Ijinus » hydraulic sensor
Publications:
• 2013, M. Fu and all, Evaluation of Sulfide Production Models
in Pressure Sewers, 11th IWA Conference on Instrumentation
Control and Automation, Narbonne – France
• 2012, J. Gouello, D. Jung, F. Gerfaud, A. Ponthieux, V. Parez, C.
Renner, Etude de l’émission de l’hydrogène sulfuré au sein d’un
poste de relèvement d’un réseau d’assainissement, 20èmes
Journées Information Eaux, Poitiers - France
• Learn more at the upcoming Astee congress in Orleans (June 3rd-6th)
Business references:
Avanced waste water monitoring has been deployed
in different regions (Bretagne, Champagne…)
Source : S::can

18. Managing waste water networks
Matrics project (started 2014)
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o 5
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19. Protecting the environment and health
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Ressources
Drinking
water
Waste
Water
Protection

20. Storms can cause flooding, sewer overflow and
pollution
The combination of waste water network monitoring,
weather forecast, network modeling and optimization
algorithms helps to properly operate storage tanks and
pumps in order to reduce combined sewer overflow
Protecting the environment and health
Preventing combined sewer overflow
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Source : RainGain project
Publications:
• 2010, Beraud and all , Optimisation of sewer networks hydraulic
behaviour during wet weather: coupling genetic algorithms with
two sewer networks modelling tools
• Optimal Management of Sewer Networks during Wet Weather
Event by Stochastic Dynamic Programming (to be published at
European Symposium on Computer Aided Process Engineering -
15-18 juin Budapest)
• http://www.raingain.eu/en/scientific-publications
Business references:
STAR control® (Krüger) implemented for the integrated control of
sewer systems and wastewater treatment plants (Copenhagen)
In close cooperation with the ENSCP, we are testing new
polarimetric X-band radars to provide fine-scale rainfall
data in the RainGain project
We are improving the optimization strategy with stochastic
dynamic programming to take into account the
randomness of storms and rain

21. Source : VERI
GIRAC (collaborative project)
Sometimes, you just can not prevent
pollution and overflow from combine
sewer systems …
But at least you can protect the health of
the citizens if you anticipate them
With the connection of rainfall sensors
with river, sewer and sea modelling, you
can forecast and manage the quality of
bathing water
GIRAC was a collaborative project,
labelled by two competitiveness clusters
(Mer Bretagne and Mer Méditerranée)
Protecting the environment and health
GIRAC (Gestion Intégrée des Rejets d’Assainissement Côtiers)
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Business references:
Girac has been tested in four cities and the service was operational
last summer in Antibes
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22. Wrap-up
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23. Complex supply chain to set up!
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Measure
(Measure of water quality parameters)
Communication
(Data transmission, Wireless solution)
Data storage
(Data acquisition and storage)
Data Treatment
including simulation
(How to transform data into information)
Decision Support Tool
(What we need to do)
Key technologies
Manufacturing
(Sensors, communication devices)
Installation
(Manhole, communication network)
Maintenance
(Replace probes, change battery packs)
Software development
(IT development)
and model calibration
Control and React
(Water supply services, manage alarms)
Key Services

24. At first glance, sensors means increase cost…
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True only if data are not transformed into information to Act
COSTSBENEFITS
DATA
Expertise
&
Know-how

25. After transformation, benefits appear…
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DATA
INFORMATION + ACTION = SUSTAINABLE BENEFITS
COSTSBENEFITS
Expertise
&
Know-how

26. Veolia Smart Water Quality Offer Objectives
27
Thanks to an ongoing R&D investment and well chosen partnerships,
Veolia can provide the most advanced « smart water » solutions for
its customer, all along the water cycle.
Understand
Water Networks
are such a black
box
Open the black box.
Observed and
understand
waterworks effect
and network
mangement choice
Optimize
Optimize network
operation an
management
Renewal strategy,
Reagent/Energy
consumption
early failure
detection avoid
services troubles
Survey
Detect in real-
time any
abnormal change
of water quality
Network evolve
continuously, dozen
waterworks are
done
simultaneously
Each one increase
fault probability