controlled variables selection for a biological wastewater treatment process

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


Outline

Background

Operational Objectives

Degrees of Freedom Analysis

Controlled Variables (CV) Selection

Proposed Control Structure

Conclusion



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


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


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



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



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



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%



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



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



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



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



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.



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




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


Scaled gains for candidate CVs


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



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!


Flow rates Chemical Oxygen Demand (COD) Nitrogen Sludge Volume Index (SVI)

Initial

Optimized


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



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


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

controlled variables selection for a biological wastewater treatment process

Documents