Operational control based on model predictive control for Drinking Water Networks and other challenges and opportunities for research and development. Presentation of the developments of the FP7-funded EU research project EFFINET (see http://effinet.eu)

1. DWN Management
Challenges and opportunities

2. Project Goals
¡ Devise algorithms and software for the
operational management of drinking water
networks to control pumping and valve
operations in real time in a profitable and
risk-averse manner,
¡ Optimal placement of bids in the day-ahead
market to complement existing bilateral
contracts,
¡ Early and systematic detection of leaks for
the minization of non-revenue water and
¡ Detection of contaminations.

3. Part I: Control

4. Control Module Goals
¡ Reduce energy consumption for pumping,
¡ Meet the demand requirements,
¡ Keep the storage above safety limits,
¡ Respect the technical limitations: pressure limits,
overflow limits & pumping capabilities,
¡ Have foresight (predict how the water demand
and energy cost will move and act accordingly).

5. Control Challenges
The control module of a DWN should take into
account:
¡ The volatility in water demand,
¡ The volatility in energy prices (€/kWh),
¡ Reconstructed online measurements
(measurements often come from faulty sensors or
are not accessible),
¡ Operational constraints.

6. 3380 3400 3420 3440 3460 3480 3500 3520 3540 3560
0
2
4
6
8
10
12
x 10
−3 Prediction Error
Past Data
Observed
Forecast
The Control Module
Energy Price
Water Demand
Drinking Water
Network
Online
Measurements
Flow
Pressure
Quality
Forecasting
Module
History
Data
Data Validation
Module
Validated
Measurements
Commands
Model
Predictive
Controller

7. Prediction of water demand
0 20 40 60 80 100 120 140 160 180 200
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Time [h]
WaterDemandFlow[m
3
/h]
Forecasting of Water Demand
FuturePast
5 10 15 20 25
−5
−4
−3
−2
−1
0
1
2
3
4
x 10
−3
Time [hr]
PredictionError[m3/h]
Prediction of
Water Demand
Scenario Fan: A set of possible
scenarios for the evolution of
upcoming water demands.
1 2 3 4 5 6 7 8 9
−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
Time [hr]
Error[m3/hr]
Error − Scenario tree
Scenario Tree: Contains appro-
ximately the same information as
the scenario fan, but is of lower
complexity.

8. How MPC works…
Prefer to pump
when the price is
low!
Stay above the
safety storage
volume
PAST FUTURE
Volume in
tank (m3)
Time (h)
Do not overflow!
Time (h)
Pumping
(m3/h)
Avoid pumping
when the price is
high!
Account for the
pumping
capabilities
Why MPC:
¡ Optimal: Computes the
control actions by
optimizing a
performance criterion,
¡ Realistic: Accounts for
the operational
constraints,
¡ Predictive: Has foresight;
acts early before the
price or the demand
changes.

9. MPC: Performance
10 20 30 40 50 60 70 80 90
0.2
0.4
0.6
0.8
MPC Control Action (1~20)
ControlAction
10 20 30 40 50 60 70 80 90
0.2
0.4
0.6
0.8
MPC Control Action (21~46)
ControlAction
10 20 30 40 50 60 70 80 90
0
0.1
0.2
Time [hr]
WaterCost[e.u.]
MPC in action
• 88 demand nodes
• 63 tanks
• 114 pumping stations
• 17 flow nodes
50 100 150 200 250 300 350 400 450 500
4
5
6
7
8
Economic Cost (E.U.)
50 100 150 200 250 300 350 400 450 500
0.5
1
1.5
2
Smooth Operation Cost
0 50 100 150 200 250 300 350 400 450 500
0
2
4
6
Safety Storage Cost (× 107
)
Low price à Pumping
The system operator has
information about the
current and the
predicted operation cost.

10. 5 10 15 20 25 30 35 40 45 50 55
0
20
40
60
80
100
Closed−loop MPC Simulation
Time [hr]
Repletion[%]
5 10 15 20 25 30 35 40 45 50 55
0
0.5
1
1.5
Time [hr]
Demand[m
3
/s]
MPC: Performance
10 20 30 40 50 60 70 80 90
0.2
0.4
0.6
0.8
MPC Control Action (1~20)
ControlAction
10 20 30 40 50 60 70 80 90
0.2
0.4
0.6
0.8
MPC Control Action (21~46)
ControlAction
10 20 30 40 50 60 70 80 90
0
0.1
0.2
Time [hr]
WaterCost[e.u.]
Foresight: Tanks starts
loading up before a
DMA asks for water.

11. Clear Economic Benefit!
¡ MPC outperforms the currect control solution for
the Barcelona case study,
¡ Reduction of production and transporation
costs*.
* A.K. Sampathirao, J.M. Grosso, P. Sopasakis, C. Ocampo-Martinez, A. Bemporad and V. Puig, Water demand forecasting for the optimal operation of
large-scale drinking water networks: The Barcelona Case Study, 19th IFAC World Congress, Cape Town, South Africa.

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0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
x 10
4
Time [hr]
Volume[m3
]
Safety Volume
Minimum Volume
Maximum Volume
MPC Upper Bound
MPC Lower Bound
Predicted Trajectory
Closed−loop trajectory
Modelling of the
uncertain demand
time series.
Hydraulic model for the
DWN of Barcelona with flow
and pressure dynamics.
Definition of the control
architecture for a DWN
using MPC and demand/
price forecasters
`w
(uk, k) , W↵ (↵1uk + ↵2,kuk) ,
`s
(xk) , s0
kWxsk, sk , max {0, xs
xk}
` ( uk) , ( uk)0
Wu uk
Definition of the technical
and economic objectives
for the operation of the
water network
50 100 150 200 250 300 350 400 450 500
4
6
8
Economic Cost (E.U.)
50 100 150 200 250 300 350 400 450 500
2
4
6
Smooth Operation Cost
0 50 100 150 200 250 300 350 400 450 500
0
2
4
6
Safety Storage Cost (× 10
7
)
Estimation of the online
operating and
economic costs.Formulation of the MPC
problem taking into
account the associated
uncertainty
EFFINET: Developments

13. EFFINET: Developments
!
20 40 60 80 100 120
2
3
4
5
6
7
x 10
4 d100CFE
time (h)
m3
20 40 60 80 100 120
200
300
400
500
d114SCL
time (h)
m3
20 40 60 80 100 120
1000
2000
3000
4000
d115CAST
time (h)
m3
20 40 60 80 100 120
0.5
1
1.5
x 10
4 d130BAR
time (h)
m3
20 40 60 80 100 120
500
1000
1500
2000
2500
3000
d132CMF
time (h)
m3
20 40 60 80 100 120
400
600
800
1000
d135VIL
time (h)
m3
20 40 60 80 100 120
200
400
600
800
1000
d176BARsud
time (h)
m3
20 40 60 80 100 120
500
1000
1500
2000
2500
3000
d450BEG
time (h)
m3
20 40 60 80 100 120
1000
2000
3000
d80GAVi80CAS85
time (h)
m3
Simulator
Real data
Upper limit
Safety level
Validation of the
hydraulic model against
real data
Efficient MATLAB simulator
that allows a very
productive in-silico
simulation of a DWN in
closed loop with an MPC.
Up-to-date Simulink
simulator with an MPC-
based control and a
(sensor) fault detection
module.
Implementation of
numerical optimisation
routines on GPUs.

14. Stochastic MPC
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
height = 8
Motivation:
¡ We may not assume that we have
exact knownledge of the future water
demand and electricity price,
¡ Probabilistic information is available for
the future demand and price evolution,
¡ We need to optimize the expectation of
the cost (with respect to the
constraints),
¡ A certain risk for not satisfying the
demand requirements can be
allocated beforehand.

15. Part II: Leak &
Contamination Detection

16. Monitoring Module Goals
¡ Independent infrastructure to detect and isolate
leakages and contamination events,
¡ Minimization of non-revenue water (most water
transportation systems waste a 20% of the water)*
¡ Estimate the magnitude of a leakage or
contamination
¡ Detect faulty sensors,
¡ Optimize the placement of sensors in the
network.
* For the Barcelona DWN, this sums up to more than 80M€/year.
In practice, things
can go wrong…

17. Leakage detection
Combination of technologies (hardware/software):
¡ Online measurements (pressure, flow),
¡ Manual measurements,
¡ Software: algorithmic solutions. Repairment: Portable
equipment for in-situ
detection.
Measurements are
used by the leakage
detecion software.Measurements are
collected by the
central system.

18. Monitoring architecture
• Flows/Pressures, (DMAs/AMRs)
• Quality data
• Levels, Pumps, Valves
• Consumer complaints
• Data gathered manually
Flow meters, pressure
meters, level meters,
state of the pumps &
valves
Demand forecasts Control Actions
Alarms

19. Leakage detection
algorithm
¡ Makes use of a hydraulic model of the network
and compares the actual and the predicted
(ideal) state of the network (pressures, flows),
¡ Examines whether there is a possible leakage at
some place in the network,
¡ If the leakage is confirmed, it alters the network
operator,
¡ Tries to locate the leakage (using series data from
the available sensors).
Demonstrable results à Controlled leaks in the networks of Barcelona
and Limassol were detected by computer algorithms!

20. Contamination detection &
isolation algorithm
¡ Uses online measurements from quality sensors,
¡ If a contamination event is confirmed, the
software predicts its spread across the network
and suggests the possible isolation of parts of the
DWN.
¡ After in-site measurements, the network operator
resolves the issue.

21. Thank you for your attention.