Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
The Seventh Italian Conference on Chemical
and Process Engineering
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
Control Structure
Analysis for an
Activated Sludge
Process
Outline
Mulas, Skogestad
Outline
 Motivations
 Plant Description
 Process Model
 Control Structure Analysis
 Results
 Conclusions
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Motivations
Mulas, Skogestad
Outline
Motivations
Wastewater treatment processes (WWTP) can be considered the largest
industry in terms of volumes of raw material treated
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
ICheaP-7
More efficient procedures for WWTP
management and control
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Objectives
Mulas, Skogestad
Outline
Motivations
Objectives
WWTP are generally operated with only elementary
control systems
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
cannot be numerically quantified
and
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?
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Plant Description
Mulas, Skogestad
Outline
Motivations
Objectives
Plant Description
The Control Structure Analysis is
applied to a real plant, the
TecnoCasic wastewater plant, located
near Cagliari (Italy)
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
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Nitrogen Removal
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
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
Objectives
Plant Description
Process Model
• Bioreactor
ASM No 1
NO3-
Denitrification
3O2 + N 2
NH 4+ + 2O2
Nitrification
13 State Variables
NO3- + 2H + + H 2O
soluble
13 State Variables
particulate
8 Reaction Rates
8 Reaction Rates
19 Stoichiometric and Kinetic
19 Stoichiometric and Kinetic
19 Stoichiometric and Kinetic
Coefficients
Coefficients
Coefficients
Anoxic Zone
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16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Aerobic Zone
Dissolved Oxygen (DO) Control
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Process Model
Secondary Settler
Mulas, Skogestad
Effluent
Outline
Motivations
Clarification
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Takács Layered
Model
RAS
Ref. Takács et al., 1997
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WAS
Thickening
The settler is modelled as a
stack of layers. The
concentration within each
layer is assumed to be
constant
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
No biological reactions occur
Takács Model
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Process Model
Mulas, Skogestad
Outline
Motivations
Objectives
A representation of the TecnoCasic plant can be implemented in
several different ways, using different software and simulators
Plant Description
Process Model
Matlab/ Simulink
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
Test Motion
TecnoCasic Plant Data
Off-Line measurements:
 Chemical Oxygen Demand (COD)
 Nitrogen available every two or three days
Sludge Volume Index (SVI)
On-Line measurements:
 Flow rates
 DO concentration in the basin
 Temperatures
Simulink
Exp Data
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
Control Structure Analysis
Find candidate controlled variables with good selfoptimizing properties
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
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16-18 May 2005
Bottom-Up Design
 Define operational objectives
 Identify degrees of freedom
 Identify primary controlled variables
 Determine where to set the production rate
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
“Top-Down” Analysis
Step 1
“Identify operational constraints and preferably a scalar cost function to be minimized”
Motivations
Cost Function
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
The energy consumption in terms of
aeration power represents the major
economic duty in our ASP
• Step 1
Constraints
Nitr
+ Qair
Constraints
Top-Down Analysis
Cost Function
DeNitr
J = Qair = Qair
Effluent Constraints:
defined by the legislation
requirement for the effluent
Operational Constraints:
 DO concentration
 Food-to-Microorganisms Ratio
Sludge Retention Time
Disturbances
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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
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
“Top-Down” Analysis
Step 2
“Identify dynamic and steady-state degrees of freedom (DOF)”
Motivations
Objectives
Dynamic or
Control DOF
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Nm = 5
7
Test Motion
Top-Down Analysis
Optimization
DOF
• Step 1
• Step 2
Degrees of Freedom
N opt= 3
N opt, free = N opt - N active
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16-18 May 2005
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 = 1
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
“Top-Down” Analysis
“Which (primary) variable should we control?”
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
Top-Down Analysis
LWAS = JWAS (WAS , d )- J opt (d )
• Step 1
• Step 2
• Step 3
Controlled
Variables
Step 3
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
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Closed-Loop Strategies: we adjust WAS in a feedback fashion in an
attempt to keep the controlled variable at its setpoint
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
Top-Down Analysis
• Step 1
• Step 2
• Step 3
“Top-Down” Analysis
Step 3
“Which (primary) variable should we control?”
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).
Controlled
Variables
c1=SRT
c2=F/M
Closed Loop
c3=TNp
c4=WAS
Open Loop
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Results
Mulas, Skogestad
20
5800
Outline
5600
Motivations
15
Process Model
• Bioreactor
• Secondary Settler
Test Motion
SRT [d]
5400
3
Plant Description
Cost Function [m /d]
Objectives
10
5200
5
5000
0
Top-Down Analysis
• Step 1
• Step 2
• Step 3
0
50
100
150
200
250
300
350
250
300
350
3
4800
WAS [m /d]
73.5
4600
0
Results
50
100
150
200
250
300
350
3
3
Effluent COD [gCOD/m ]
WAS [m /d]
The cost function J goes down
as the waste flowrate increases
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
73
72.5
0
50
100
150
200
3
WAS [m /d]
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Results
Mulas, Skogestad
Outline
Motivations
Positive Deviation
Objectives
c1 = SRT Closed Loop
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
Top-Down Analysis
• Step 1
• Step 2
• Step 3
Results
Negative Deviation
Positive Deviation
Negative Deviation
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
d1=COD
38628
24589
38650
24758
d2=TKN
33648
26968
33749
26991
d3=TSS
30255
29594
34171
29607
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
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway
Control Structure
Analysis for an
Activated Sludge
Process
Mulas, Skogestad
Outline
Motivations
Objectives
Plant Description
Process Model
• Bioreactor
• Secondary Settler
Test Motion
Top-Down Analysis
• Step 1
• Step 2
• Step 3
Results
Conclusions
Conclusions
In this work we have considered alternative controlled variables for
the TecnoCasic activated sludge process
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
ICheaP-7
16-18 May 2005
Dipartimento di Ingegneria Chimica e Materiali
Università di Cagliari, Italy
Chemical Engineering Department
NTNU, Trondheim, Norway