Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Controlled Variables Selection for a Biological Wastewater Treatment Process
Michela Mulas1, Roberto Baratti2, Sigurd Skogestad1
1 Department of Chemical Engineering, NTNU, Trondheim (Norway)2 Dipartimento di Ingegneria Chimica e Materiali, Università di Cagliari (Italy)
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Outline
Background
Operational Objectives
Degrees of Freedom Analysis
Controlled Variables (CV) Selection
Proposed Control Structure
Conclusion
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Motivation and Objective
Environmental water protection has gained an increasing public awareness
In a biological WWTP, the Activated Sludge Process (ASP) is the most common used and important technology to remove organic pollutant from wastewater
Show how optimal operation can be achieved in practice by designing the
ASP control system appropriately
Objective
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Stricter standards for operation of wastewater treatment plants (WWTP)
European Directive 91/271/EEC
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
However: Wastewater treatment plants are generally operated poorly with only elementary control systems
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Case StudyActivated 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 Anoxic zone (Denitrification) followed by an aerobic zone (Nitrification)
Both zones are modeled using the Activated Sludge Process Model No.1 (ASM1)
Settler
Thickening and clarification
Modeled as a stack of layers using the Takacs Model
The models are coupled together in a Matlab/Simulink environment
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
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 structureJ. Process Control, 10:487-507, 2000
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 1: Operational Objectives
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
J 1T
(kE 0.04 Qr (t)Qw (t) t0
t0T
24 0.4032kla,i2 (t) 7.8408k la,i (t) )i1
n
kDTSSw (t)Qw (t))dt
Cost Function J
J. B. Copp - COST action 624 - The COST simulation benchmark: description and simulator manualTechnical Report, European Community, 2000
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 1: Operational Objectives
The cost should minimized subject to some constraints
Constraints and Disturbances
Operational Constraints Oxygen in both reactor zones
Nitrate in anoxic zone
Food-to-Microorganisms ratio
Effluent ConstraintsDefined by the legislation requirement for the effluent
A waste water treatment plant is subject to large disturbances
Inflow Qin 6152 m3/d ± 20%
Inflow COD (chem. ox. demand) 221 g/m3 ± 20%
Inflow TKN (nitrogen) 22 g/m3 ± 20%
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
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
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 3: Optimal operation
In our plant aeration is responsible for 99% of the total cost
Setpoint for Dissolved Oxygen (DO) must be optimized
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
Cost [€/d] 2200 1466
A remarkable cost reduction with respect to the existing
conditions is observed †
Qr /Qin
Qw
A preliminary optimization was carried out to find the setpoint values for the DO in both anoxic and aerated zones
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
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
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 4: Controlled Variables Selection
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, (G’)
General approach to find “self-optimizing” CVs
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Maximum gain rule: Derivation
u
cost J
uopt
c = G 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.
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 4: Controlled Variables Selection
The following candidate CVs are suggested:
Recycle flow ratio (Qr/Qin)
Sludge Retention Time (SRT)
Food-to-Microorganisms ratio (F/M)
Effluent Ammonia
Mixed Liquor Suspended Solids (MLSS)
Nitrate in the last anoxic zone
(SNHeff )
(SNOp,3 )
Qr/Qin SRTsp F/Msp MLSS
1.49 9.77 0.74 0.17 1482 0.78
SNHeff ,sp SNO
p3,sp
Their setpoint values are the average of the optimal at various operation points
Candidate CVs
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 4: Controlled Variables Selection
CVs
c1 (Qr/Qin)const- SRT 6.50
c2 (Qr/Qin)const- F/M 1.004
c3 (Qr/Qin)const- 1.338
c4 (Qr/Qin)const- MLSS 32.20
c5 SRT - MLSS 0.13
c6 SRT - F/M 1.00
c7 SRT - 0.83
c8 SRT - MLSS 1.49
c9 F/M - 0.76
c10 F/M - 0.00
c11 F/M - MLSS 0.86
c12 F/M - 1.14
c13 - 1.02
c14 MLSS - 1.41
SNHeff
SNHeff
SNOp,3
SNHeff
SNOp,3
SNHeff SNO
p,3
SNOp,3
Candidates c5 to c14 use also recycle flow Qr as a MV
Candidates c1 to c4 have the recycle ratio fixed at its optimum and SRT, F/M, and MLSS controlled by Qw
The best configurations (with a large minimum singular value) are c1 and c4: Both have a constant recycle ratio
One feedback loop
Two feedback loops
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Scaled gains for candidate CVs
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 5: Analysis of proposed control structure
ConfigurationCost [Euro/d]
Nom. d1 d2 d3
c1 Qr/Qin- SRT 1440 1739 1752 1993
c2 Qr/Qin- F/M 1460 1775 1773 2032
c3 Qr/Qin- 1479 1832 1759 2038
c4 Qr/Qin- MLSS 1446 1632 1752 1869
(Qr/Qin- Qw) - Open Loop 1466 1777 1783 2046
d4 d5 d6
c1 Qr/Qin- SRT 1440 2390 2442 2779
c4 Qr/Qin- MLSS 1446 2056 2269 2344
c8 SRT - 1481 2470 2440 2805
c14 MLSS - 1490 2045 2257 2552
(Qr/Qin- Qw) - Open Loop 1466 2436 2458 2823
SNHeff
SNOp,3
SNOp,3
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
Evaluation of cost for some disturbances
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 5: Analysis of proposed control structureDynamic Simulations
Influent data from real plant
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
A considerably cost reduction is obtained!
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Flow rates Chemical Oxygen Demand (COD) Nitrogen Sludge Volume Index (SVI)
Initial
Optimized
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
Step 5: Analysis of proposed control structureDynamic Simulations
Initial
Optimized
Constant Influent Flow Rate
Further check: Typical variations in dry weather conditions are simulated using the variations proposed by Isaac and Thormberg
Variable Influent Flow Rate
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
The cost is reduced in both situations
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Dycops2007 S. Skogestad | Controlled Variables Selection for Biological Process
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
Conclusion
Biological wastewater treatment plant: Potential for large improvements in operation
Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion
Use systematic procedure
• Waste sludge flow controls mixed liquor suspended solids, MLSS
• Recycle ratio Qr/Qin is constant
• Air: DO setpoints at their optimal values