Modelling and Optimal Control of a Sewer...
Transcript of Modelling and Optimal Control of a Sewer...
Modelling and Optimal Control of a Sewer Network
C.1 Hydraulic application (6.9.2012)
Georges Schutz, David Fiorelli, Stefanie Seiffert, Mario Regneri, Kai Klepiszewski
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Overview
Case study• The system that is analyzed and controlled
Modelling and calibration• Use of different model types• What are the models• Model calibration & comparison
Model predictive control• MPC Principals• Objectives of MPC underlying optimization• Results based on simulation
Implementation issuesConclusions
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The studied sewer system
Artificial Lake, mainly used for drinking waterCombined sewer network to drain to central WWTP24 controllable CSOTs
• 8 are in operation nowMix of pressured and free flow pipes
Haute-Sûresto
rage l
ake
River Sûre
Sc hli rbach
2000 m
RingelTadlermühle
Tadler
Kuborn
HeiderscheidEschdorf
Hiereck
Bavigne
Flébour
Baschleiden
Boulaide
Buurgfried
Insenborn
Nothum
Mecher
Liefrange
Goesdorf
WWTP
Dahl
Büderscheid
HeischtergronnEsch/Sauer
Kaundorf
Nocher
Lultzhausen
Bockholtzmühle
Nocher-Route
concrete dam
Pumping stationCombined sewage overflow tankSewerSewer system in operation 2010Subcatchment / VillageCatchment border
River Sûre
Summer 2010
Population equivalents
3869
Impervious surface 80 ha
CSOT 8
Total CSOT volume 1600 m 3
PS 4
Total transport sewer length
19.7 km
Pressure conduits 4.1 km
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Modelling and calibrationUse of different model types
Hydrodynamic model• Planing, case studies, network analysis• In the context of the research project and the
project planning• Creates data for the calibration of the other
models (offline virtual reality)Simple time-delayed model
• Used inside to the model predictive control• Implemented in Matlab for offline simulations
and Python for the MPC pilot implementationHydrological model
• Used for long time simulations and as virtual reality for testing the MPC approach
• Integrated Control Sewer & WWTP (PhD)
Pro
ject
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ealiz
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Phas
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Acco
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sear
ch p
roje
ct
Net
wor
k op
erat
ion
MP
C p
ilot
MP
C a
pplic
atio
n
Hyd
rody
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ic m
odel
Hyd
rolo
gica
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odel
Tim
e-lin
e
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Modelling and calibrationFlow time and profile test
Test design and properties• dry weather situation• Impounding of the dry weather flow
in CSOTs• Release of the throttled volumes in a
given temporal sequence for each CSOT
Test goal• Allocation of the QinWWTP pattern to
QoutCSOT,i
• identification of individual flow times from each CSOT to the WWTP
• Validation of the hydrodynamic model• Evaluation of the other models
Qout_K
Qout_G
Qout_N
Δh Q in_w
Qou
t_W h
TemporalMeasurement
t
Q K
t
Q G
t
Q N
t
Q
K GN
WWTP
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Modelling and calibrationResult and discussion
• Hydrodynamic model fits quite good to the monitored flows• NL hydrological model much less detailed compared to the HD-
Model but is able to reproduced most of the dynamics.• Translation Model gets the timing of of the waves good enough for
the use in the MPC context.
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Model predictive controlused principals
System• Measurements• Historic data• Controllable aggregates
Optimizer• Input forecasting• Model based output prediction• Objective-function• Optimization over prediction horizon
Control loop• Apply control for u(k+1)• Run the optimization in real time (Δ )t• Update to current system state
past future
k k+Hp
k+Hccontrol horizon
prediction horizon
Predicted Output ym
Predicted control inputs u
Inside MPC
Objectif
Optimiser
System
CommandsObjectives
Qout_K
Qout_G Qout_N
WWTP
QA(t) = Qout_1(t-12) + Qout_2(t-8)
QB(t) = QA(t-7)
QC(t) = QB(t-9)
QC(t) = Qout_1(t-28) + Qout_2(t-24) Output
r (t ) u (t) y (t)
Measurements
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Model predictive controlfor the proposed sewer systems
Multi-Objective function
• Homogenous distribution of the storage
• Constant inflow to the WWTP
• Minimum overflow
φ1(n)=∑i=1
N
[V i(n)− V imax
∑i=1
N
V imax∑i=1
N
V i(n)]2
φ2(n )=[yref (n)−∑i=1
N *
Out i(n−d i)]2
φ3(n)=∑i=1
N
[Ovi(n )−NL ]2
subject to ci(x )=0 i∈Eci(x )≥0 i∈I
minimize J=∑n= t
t+Hp
λ φ1(n )+βφ2(n )+αφ3(n )
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Model predictive controlsome results
0 1 0 2 0 3 0 4 0 5 00
5
1 0
CS
OT
#2
T i m e ( h )
0 1 0 2 0 3 0 4 0 5 00
2 0
4 0
CS
OT
#3
T i m e ( h )0 1 0 2 0 3 0 4 0 5 0
0
5
1 0
1 5
CS
OT
#24
T i m e ( h )
0 1 0 2 0 3 0 4 0 5 0
02468
1 01 21 41 6R
ain
In
ten
sity
(m
m/h
)0 1 0 2 0 3 0 4 0 5 0
2 03 04 05 06 07 08 09 01 0 0
M a x
R e f
Flo
w (
m3 /1
0min
)
T i m e ( h )
R a i n i n t e n s i t y a n d s e w e r o u f l o w t o t h e W W T P
o u t f l o w ( m 3 / 1 0 m i n )
n o r m a l i z e d v o l u m e
n o r m a l i z e d o v e r f l o w
m a x i m u m o u t f l o w ( m 3 / 1 0 m i n )m a x i m u m n o r m a l i z e d v o l u m eh i g h e s t n o r m a l i z e d o v e r f l o w
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Model predictive controlsome results
-24%
Ref
StaticTheoretic
GPC (forecasting sensitivity)GPC (weighting sensitivity)
GPC (Qwwtp_REF sensitivity)
28000
30000
32000
34000
36000
38000
40000
Ove
rflo
w [m
3 ]
Static GPCforecasting sensitivity
TheoreticalGPC
weighting sensitivity
GPCQwwtp_ref sensitivity
-5%
-12%
-5%
-9%
-5%
-19%
100%
0%
Reduction of overflow volume...
Compared to a static controlphysically possible
80%
20%
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Implementation
PLS – SCADA• centralize all
network variables• Via real time bus• User and Admin
interface for the GPCOPC interface
• Industrial standard• Exists for most SCADA hardware• Software libraries available in FOSS
Global Predictive Control implementation• Matlab → Python• End-User system feedback / training / issues• Fall-back strategies
CRP PC
PLS
V1
In1
Px
PLC1
RT-Bus
OPC ServerRT-Bus ↔ OPC
P1
Out1
OPC
Global predictiveControl
Local Control
Vi
Ini
PiOut
i
PLCi
Local Controls
Weighted sub-goals
αMinimize overflow
βConstant WWTP-inflow
λHomogeneous
storage use
Convex Optimization
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Conclusions
Different models used in different phases of the projectVirtual reality
• important to demonstrate the possible gains of the GPC approach• important to analyze different control approaches• validate the effective gains of the controlled system after
implementationSimple sewer model (time delayed / plug flow)
• linear, robust model• calibration towards the real network affordable• convex optimization
Further works• Integrate a global quality component in the simple model• Handling structural network modifications in the GPC approach