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Controlled Variables Selection for a Biological Wastewater Treatment Process

Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion. Controlled Variables Selection for a Biological Wastewater Treatment Process. Michela Mulas 1 , Roberto Baratti 2 , Sigurd Skogestad 1.

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Controlled Variables Selection for a Biological Wastewater Treatment Process

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  1. 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 of Chemical Engineering, NTNU, Trondheim (Norway) 2 Dipartimento di Ingegneria Chimica e Materiali, Università di Cagliari (Italy)

  2. 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

  3. European Directive 91/271/EEC In a biological WWTP, the Activated Sludge Process (ASP) is the most common used and important technology to remove organic pollutant from wastewater Objective Show how optimal operation can be achieved in practice by designing the ASP control system appropriately Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion Motivation and Objective Stricter standards for operation of wastewater treatment plants (WWTP) Environmental water protection has gained an increasing public awareness 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

  4. 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

  5. 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

  6. 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 J. B. Copp - COST action 624 - The COST simulation benchmark: description and simulator manual Technical Report, European Community, 2000

  7. 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 Effluent Constraints • Oxygen in both reactor zones • Nitrate in anoxic zone • Food-to-Microorganisms ratio Defined 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%

  8. Background Operational Objective DOF AnalysisCV 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

  9. Q Q / Q r w in A remarkable cost reduction with respect to the existing conditions is observed 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 †

  10. 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

  11. 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) s Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion Step 4: Controlled Variables Selection General approach to find “self-optimizing” CVs • 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’)

  12. Maximum gain rule: Derivation cost J c = G u u uopt Halvorsen, I.J., S. Skogestad, J. Morud and V. Alstad (2003). ”Optimal selection of controlled variables”. Ind. Eng. Chem. Res. 42(14), 3273–3284.

  13. eff p , 3 ( S ) ( S ) NH NO p 3 , sp S NO eff , sp S NH 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) • Effluent Ammonia • Mixed Liquor Suspended Solids (MLSS) • Nitrate in the last anoxic zone Their setpoint values are the average of the optimal at various operation points

  14. s p p p p , , , , 3 3 3 3 S S S S NO NO NO NO eff eff eff eff S S S S NH NH NH NH The best configurations (with a large minimum singular value) are c1 and c4: Both have a constant recycle ratio Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion Step 4: Controlled Variables Selection Scaled gains for candidate CVs 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

  15. p p , , 3 3 S S NO NO eff S NH Background Operational Objective DOF Analysis CV Selection Proposed Control Structure Conclusion Step 5: Analysis of proposed control structure Evaluation of cost for some disturbances With inflow Qinconstant (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

  16. Initial Optimized 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) A considerably cost reduction is obtained!

  17. Initial Optimized 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

  18. 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 FreedomAnalysis • 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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