1. Control Structure Design for an Activated Sludge Process
Michela Mulas1,2, Sigurd Skogestad2
1 Dipartimento di Ingegneria Chimica e Materiali Università degli Studi di Cagliari, Italy
2 Chemical Engineering Department
NTNU, Trondheim, Norway

2. Outline Motivations Plant Description Process Model
Control Structure Analysis
Results
Conclusions

3. More efficient procedures for WWTP management and control
Motivations
Wastewater treatment processes (WWTP) can be considered the largest industry in terms of volumes of raw material treated
Outline
Motivations
Industrial expansion and urban population growth have increased the amount and diversity of wastewater generated
Because of the most recent guidelines and regulation which require the achievement of specific standards to the treated wastewater, a great effort has been devoted to the improvement of treatment processes
The WWTP has become part of a production process, e.g. for fresh water reuse purpose
More efficient procedures for WWTP management and control

4. Objectives
WWTP are generally operated with only elementary control systems
Outline
Motivations
Objectives
The problems are:
the inflow is variable, in both quantity and quality
there are few and unreliable on-line analyzers
most of the data related to the process are subjective and cannot be numerically quantified
With a proper control structure design we might implement the optimal operation policy for an ASP
Which variables should be measured, which inputs should be manipulated and which link should be made between the two sets?

5. Plant Description Nitrogen Removal
The Control Structure Analysis is applied to a real plant, the TecnoCasic wastewater plant, located near Cagliari (Italy)
Outline
Motivations
Objectives
Plant Description
The Activated Sludge Process (ASP) is the most widely used system for biological treatment of liquid waste
ASP involves a biological reactor and a settler where from the biomass is recycled to the anoxic basin
Nitrogen Removal

6. 19 Stoichiometric and Kinetic Coefficients
Process Model
Bioreactor
The Activated Sludge Model No.1 (Henze et al.,1987) is the state of art model when the biological phosphorus removal is not considered
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
ASM No 1
Denitrification NO 3 - O 2 + N
Nitrification NH 4 + 2 O NO 3 - H
soluble
13 State Variables
13 State Variables
particulate
8 Reaction Rates
8 Reaction Rates
19 Stoichiometric and Kinetic
Coefficients
19 Stoichiometric and Kinetic Coefficients
19 Stoichiometric and Kinetic
Coefficients
Anoxic Zone
Aerobic Zone
Dissolved Oxygen (DO) Control

7. No biological reactions occur
Process Model
Secondary Settler
Ref. Takács et al., 1997
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Takács Layered
Model
Effluent
Clarification
Thickening
WAS
RAS
When entering the settler, all the particulate components in the ASM1 model are lumped into a single variable X. The reverse process is performed as for the outlet
The settler is modelled as a stack of layers. The concentration within each layer is assumed to be constant
No biological reactions occur
Takács Model

8. Process Model Matlab/ Simulink
Outline
Motivations
Objectives
Plant Description
Process Model
A representation of the TecnoCasic plant can be implemented in several different ways, using different software and simulators
Matlab/ Simulink

9. Test Motion TecnoCasic Plant Data Off-Line measurements:
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Off-Line measurements:
Chemical Oxygen Demand (COD)
Nitrogen
Sludge Volume Index (SVI)
available every two or three days
On-Line measurements:
Flow rates
DO concentration in the basin
Temperatures
Simulink
Exp Data

10. Control Structure Analysis
Find candidate controlled variables with good self-optimizing properties
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Self-Optimizing Control is when acceptable operation can be achieved using constant setpoints for the controlled variables
The procedure proposed by Skogestad (2004) is divided in two main part:
Top-Down Design
Bottom-Up Design
Define operational objectives
Identify degrees of freedom
Identify primary controlled variables
Determine where to set the production rate

11. “Top-Down” Analysis J = Q + Step 1
“Identify operational constraints and preferably a scalar cost function to be minimized”
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Cost Function
Constraints
Cost Function
The energy consumption in terms of aeration power represents the major economic duty in our ASP J = Q
air
DeNitr +
Nitr
Constraints
Effluent Constraints: defined by the legislation requirement for the effluent
Operational Constraints:
DO concentration
Food-to-Microorganisms Ratio
Sludge Retention Time
Disturbances
In the TecnoCasic plant an equalization tank is present at the top of the ASP
The influent compositions are the disturbances which we cannot affect

12. “Top-Down” Analysis Dynamic or Control DOF N = 7 N = 5
Step 2
“Identify dynamic and steady-state degrees of freedom (DOF)”
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Degrees of Freedom
Dynamic or Control DOF N m = 7 N m = 5
Optimization DOF N
opt = 3
The optimization is generally subject to constraints and at the optimum many of these are usually “actives”, e.g. in the ASP the DO concentrations in both anoxic and aerated zone N
opt ,
free = -
active N
opt ,
free = 1

13. “Top-Down” Analysis ( ) d J WAS L - = , Step 3
“Which (primary) variable should we control?”
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Step 3
Controlled
Variables
We first need to control the variables directly related to ensuring optimal economical operation
The optimisation of a system is selecting conditions to achieve the best possible result with some limits: we are interested in steady state optimization of the ASP in the TecnoCasic plant ( ) d J
WAS L
opt - = ,
The magnitude of the loss will depend on the control strategy used to adjust the WAS flowrate during operation
Open-Loop Strategies: we want to keep the WAS flowrate at its setpoint
Closed-Loop Strategies: we adjust WAS in a feedback fashion in an attempt to keep the controlled variable at its setpoint

14. “Top-Down” Analysis Closed Loop Open Loop Step 3
“Which (primary) variable should we control?”
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Step 3
Controlled
Variables
To identify good candidate controlled variables, one should look for variables that satisfy all of the following requirements (Skogestad, 2000):
The optimal value of should be insensitive to disturbance
The controlled variable should be easy to measure and control
The controlled variable should be sensitive to changes in the manipulated variables (the steady degree of freedom).
c1=SRT
c2=F/M
c3=TNp
c4=WAS
Closed Loop
Open Loop

15. The cost function J goes down as the waste flowrate increases
Results
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Step 3
Results
The cost function J goes down as the waste flowrate increases

16. c3 = TNDeNitr Closed Loop
Results
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Step 3
Results
Positive Deviation
Negative Deviation
 c1 = SRT Closed Loop
c3 = TNDeNitr Closed Loop
d1=COD
38682
24800
38679
24816
d2=TKN
33756
27006
33765
26967
d3=TSS
34182
29607
30252
29591
c2 = F/M Closed Loop
c4 = Open Loop
38628
24589
38650
24758
33648
26968
33749
26991
30255
29594
34171
The anoxic zone behaviour can influence the overall cost function; even if the air flowrate in it is quite small compared with the aerobic part

17. Conclusions
In this work we have considered alternative controlled variables for the TecnoCasic activated sludge process
Outline
Motivations
Objectives
Plant Description
Process Model
Bioreactor
Secondary Settler
Test Motion
Top-Down Analysis
Step 1
Step 2
Step 3
Results
Conclusions
Following the plantwide control structure design procedure proposed by Skogestad (2004), we have found that a better response to influent disturbances can be obtained using as controlled variable the total Nitrogen in the anoxic zone, manipulating the WAS flowrate
That is a good starting point to understand how this kind of system can be improve
The optimization part has to be implemented and studied for systems with a different configuration
For an activated sludge plant the only steady state occurs when the process is shut down (Olsson and Newell, 2001). For that reason it will be interesting to find a kind of “dynamic” steady state and apply the top-down analysis in this case