Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Controlled Variables Selection for a Biological
Wastewater Treatment Process
Michela Mulas1, Roberto Baratti2, Sigurd Skogestad1
1 Department
2 Dipartimento
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of Chemical Engineering, NTNU, Trondheim (Norway)
di Ingegneria Chimica e Materiali, Università di Cagliari (Italy)
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Outline

Background

Operational Objectives

Degrees of Freedom Analysis

Controlled Variables (CV) Selection

Proposed Control Structure

Conclusion
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Motivation and Objective
Environmental water protection has gained an
increasing public awareness
Stricter standards for operation of
wastewater treatment plants (WWTP)
European Directive 91/271/EEC
However: Wastewater treatment plants are generally operated poorly with
only elementary control systems
Some reasons are:
 understanding of the treatment process is lacking
 reliable technologies are insufficient
 benefits of improved control are not appreciated
 WWTP is considered a non-productive process
In a biological WWTP, the Activated
Sludge Process (ASP) is the most
common used and important technology
to remove organic pollutant from
wastewater
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Objective
Show how optimal operation can be
achieved in practice by designing the
ASP control system appropriately
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Case Study
Activated Sludge Process (ASP)
We consider the ASP in the TecnoCasic
WWTP located in Cagliari (Italy)
Nitrogen and Carbon
Compounds Removal
ASP: bioreactor + settler + recycle of biomass (“sludge”)
Bioreactor
Settler
 Anoxic
zone (Denitrification) followed
by an aerobic zone (Nitrification)



Both zones are modeled using the
Activated Sludge Process Model No.1
(ASM1)
Thickening and clarification
Modeled as a stack of layers using the
Takacs Model
The models are coupled together in a Matlab/Simulink environment
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
General Procedure for Controlled Variables Selection
What should we control ?
Systematic procedure*

Step 1. Define Operational objectives (cost J) and constraints

Step 2. Degrees of Freedom (DOF) Analysis

Step 3. Optimize for various disturbances

Step 4. Controlled Variables. 1) Control active constraints
2) Control “self-optimizing” variables

Step 5. Analysis of proposed control structure
Self-optimizing control* is achieved when a constant setpoint
policy results in an acceptable process operation (without the need
to reoptimize when disturbances occur)
*S. Skogestad - Plantwide control: the search for the self-optimizing control structure
J. Process Control, 10:487-507, 2000
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 1: Operational Objectives
Cost Function J
We adopt the costs proposed in the COST Benchmark (Copp, 2000)
Three contributions to cost:

Pumping costs due to the required pumping energy

Pumping costs due to the required aeration flow (99% of total cost)

Sludge disposal costs
1 t0 T
J   (k E 0.04 Qr (t)  Qw (t)
T t0
n
2
24 i1 0.4032k la,i
(t)  7.8408k la,i (t))
k DTSSw (t)Qw (t))dt


J. B. Copp - COST action 624 - The COST simulation benchmark: description and simulator manual
Technical Report, European Community, 2000
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 1: Operational Objectives
Constraints and Disturbances
The cost should minimized subject to some constraints
Operational Constraints

Oxygen in both reactor zones
Effluent Constraints

Nitrate in anoxic zone
Defined by the legislation requirement for
the effluent

Food-to-Microorganisms ratio
A waste water treatment plant is subject to large disturbances
Inflow Qin
Inflow COD (chem. ox. demand)
Inflow TKN (nitrogen)
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6152 m3/d ± 20%
221 g/m3 ± 20%
22 g/m3 ± 20%
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 2: Degrees of Freedom Analysis
DOFs for control (valves, MVs)
Nm = 7
– given feed (influent)
-1
- Need to control two levels with no steady-state effect
-2
= Steady-state DOFs
Nopt = 4
Common: Control dissolved oxygen (DO) in both anoxic and aerated zones
-2
Remaining DOFs (need to identify CVs)
=2
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 3: Optimal operation
In our plant aeration is responsible
for 99% of the total cost
Setpoint for Dissolved Oxygen
(DO) must be optimized
A preliminary optimization was carried out to find the setpoint values
for the DO in both anoxic and aerated zones
Initial
Improved (Nominal)
DOp,sp
[gO2/m3]
0.09
0.22
DOn,sp
[gO2/m3]
4
2.5
1.14
1.49
[m3/d]
60
77
[€/d]
2200
1466
Qr /Qin
Qw

Cost
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[-]
A remarkable cost reduction
with respect to the existing
conditions is observed
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 3: Optimal operation
Manipulated Variable: Waste sludge flow
 Recycle ratio Qr/Qw fixed at its average optimal value
 Oxygen is fixed at the previously defined setpoints
The operational constraints are respected for
Qw ranging between 60 and 100 m3/d
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 4: Controlled Variables Selection
General approach to find “self-optimizing” CVs
A self-optimizing CV should be
1) accurate to measure and easy to control
2) sensitive to changes in the manipulate variables (large gain)
3) optimal value should be insensitive to disturbances (d)
Combine into the “maximum gain rule”:
 Maximize scaled gain |G’| from MV to CV.
G’ = S1 G S2
 Disturbances and cost enters into scalings
Multivariable: Use minimum singular value, s (G’)
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S. Skogestad | Controlled Variables Selection for Biological Process
Maximum gain rule: Derivation
cost J
c=Gu
uopt
u
Halvorsen, I.J., S. Skogestad, J. Morud and V. Alstad (2003). ”Optimal selection of controlled variables”. Ind. Eng. Chem. Res. 42(14), 3273–3284.
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 4: Controlled Variables Selection
Candidate CVs
The following candidate CVs are suggested:
Recycle flow ratio (Qr/Qin)
 Sludge Retention Time (SRT)
 Food-to-Microorganisms ratio (F/M)
eff
)
 Effluent Ammonia (SNH
 Mixed Liquor Suspended Solids (MLSS)
p,3
(S
 Nitrate in the last anoxic zone NO )
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Qr/Qin
SRTsp
F/Msp
1.49
9.77
0.74
Their setpoint values are the
average of the optimal at
various operation points
eff ,sp
SNH
0.17
MLSS
1482
p 3,sp
SNO
0.78
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 4: Controlled Variables Selection
Scaled gains for candidate CVs
s
CVs
c1
(Qr/Qin)const- SRT
6.50
c2
(Qr/Qin)const- F/M
1.004
c3
(Qr/Qin)const- SNH
1.338
c4
(Qr/Qin)const- MLSS
32.20
c5
SRT - MLSS
0.13
c6
SRT - F/M
1.00
c7
eff
SRT - SNH
0.83
c8
SRT - MLSS
1.49
c9
F/M -SNO
0.76
c10
F/M -SNH
0.00
c11
F/M - MLSS
0.86
c12
1.14
c13
F/M - SNO
eff
p,3
SNH -SNO
c14
MLSS -SNO
1.41
eff
p,3
eff
p,3
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p,3
1.02
Candidates c1 to c4 have the recycle
ratio fixed at its optimum and SRT, F/M,
and MLSS controlled by Qw
One feedback loop
Candidates c5 to c14 use also recycle
flow Qr as a MV
Two feedback loops
The best configurations (with a large
minimum singular value) are c1 and c4:
Both have a constant recycle ratio
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 5: Analysis of proposed control structure
Evaluation of cost for some disturbances
Cost [Euro/d]
Configuration
Nom.
d1
d2
d3
c1
Qr/Qin- SRT
1440
1739
1752
1993
c2
Qr/Qin- F/M
1460
1775
1773
2032
c3
Qr/Qin- SNH
Qr/Qin- MLSS
eff
1479
1832
1759
2038
1446
1632
1752
1869
1466
1777
1783
2046
d4
d5
d6
c4
(Qr/Qin- Qw) - Open Loop
c1
Qr/Qin- SRT
1440
2390
2442
2779
c4
Qr/Qin- MLSS
1446
2056
2269
2344
c8
SRT -
SNO
p,3
MLSS - SNO
1481
2470
2440
2805
1490
2045
2257
2552
1466
2436
2458
2823
c14
p,3
(Qr/Qin- Qw) - Open Loop
With inflow Qin constant (d1,
d2, d3): Control of Mixed
Liquor Suspended Solids
(MLSS) is the best - as
predicted by the maximum
gain rule
With Qin varying ± 20% (d4,
d5, d6):
Control of MLSS remains
the best choice
d1 = Inflow COD (chem. ox. demand): 221 g/m3 ± 20%
d2 = Inflow TKN (Nitrogen): 22 g/m3 ± 20%
d3 = d1 and d2
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 5: Analysis of proposed control structure
Dynamic Simulations
Proposed Configuration
• Waste sludge flow controls MLSS
• Recycle ratio Qr/Qin is constant
• Air: DO setpoints at their optimal values
In order to verify the system behavior, dynamic
simulations are performed
Influent data from real plant
 Flow rates
 Chemical Oxygen Demand (COD)
 Nitrogen
 Sludge Volume Index (SVI)
Initial
Optimized
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A considerably cost reduction is obtained!
S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Step 5: Analysis of proposed control structure
Dynamic Simulations
Further check: Typical variations in dry weather
conditions are simulated using the variations proposed
by Isaac and Thormberg
S. Isaacs and D. E. Thormberg - A comparison between
model and rule based control of a periodic ASP
Water Science and Technology 37(12):343-352, 1998
Constant Influent Flow Rate
Variable Influent Flow Rate
The cost is reduced
in both situations
Initial
Optimized
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S. Skogestad | Controlled Variables Selection for Biological Process
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Conclusion
Biological wastewater treatment plant: Potential for large improvements in
operation
Use systematic procedure

Step 1. Define Operational Objectives (J) and constraints

Step 2. Degree of Freedom Analysis

Step 3. Optimize for various disturbances

Step 4. Controlled Variable selection: Use Maximum gain rule for screening
• Waste sludge flow controls mixed liquor suspended solids, MLSS
• Recycle ratio Qr/Qin is constant
• Air: DO setpoints at their optimal values
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S. Skogestad | Controlled Variables Selection for Biological Process