Control Structure Analysis for an Activated Sludge Process The Seventh Italian Conference on...

Control Structure Analysis for an Activated Sludge Process The Seventh Italian Conference

on Chemical and Process Engineering

Mulas, Skogestad

Control Structure Design for an Activated Sludge

Process

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

DepartmentNTNU, Trondheim, Norway

ICheaP-7

16-18 May 2005

Control Structure Analysis for an Activated Sludge ProcessMulas, Skogestad

Dipartimento di Ingegneria Chimica e MaterialiUniversità di Cagliari, Italy

Chemical Engineering DepartmentNTNU, Trondheim, Norway

OutlineOutline

Outline

Motivations

Plant Description

Process Model

Control Structure Analysis

Results

Conclusions

ICheaP-7

16-18 May 2005




Motivations Motivations

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 generatedBecause 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

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16-18 May 2005




ObjectivesObjectives

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

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

WWTP are generally operated with only elementary control systems

With a proper control structure design we might implement the optimal operation policy

for an ASPWhich 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




Plant DescriptionPlant Description

Outline

Motivations

Objectives

Plant Description

The Control Structure Analysis is applied to a real plant, the TecnoCasic wastewater plant, located near Cagliari (Italy)

ASP involves a biological reactor and a settler where from the biomass is recycled to the anoxic basin

The Activated Sludge Process (ASP) is the most widely used system for biological treatment of liquid waste

Nitrogen Removal Nitrogen Removal

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16-18 May 2005




Process ModelProcess Model

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor

ASM No 1

The Activated Sludge Model No.1 (Henze et al.,1987) is the state of art model when the biological phosphorus removal is not considered

13 State Variablessoluble

particulate8 Reaction Rates

Aerobic Zone Aerobic Zone

13 State Variables

8 Reaction Rates

Denitrification NO3 3O2 N2

Nitrification NH4 2O2 NO3

2H H2O

Anoxic Zone Anoxic Zone

Dissolved Oxygen (DO) Control

Bioreactor

19 Stoichiometric and Kinetic Coefficients

19 Stoichiometric and Kinetic

Coefficients

19 Stoichiometric and Kinetic

Coefficients

ICheaP-7

16-18 May 2005





Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor

• Secondary Settler

Takács Layered Model

Secondary Settler

WAS

RAS

Effluent

Clarification

Thickening

Ref. Takács et al., 1997

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 occurNo biological reactions occurThe settler is modelled as a stack of layers. The concentration within each layer is assumed to be constant Takács Model

ICheaP-7

16-18 May 2005





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

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16-18 May 2005




Test MotionTest Motion

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

On-Line measurements: Flow rates DO concentration in the basin Temperatures

Off-Line measurements: Chemical Oxygen Demand (COD) Nitrogen Sludge Volume Index (SVI)

TecnoCasic Plant DataTecnoCasic Plant Data

SimulinkExp Data

available every two or three days

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16-18 May 2005




Control Structure AnalysisControl Structure Analysis

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

Find candidate controlled variables with good self-optimizing properties

Self-Optimizing Control is when acceptable operation can be achieved

using constant setpoints for the controlled variables

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:

Bottom-Up Design

Bottom-Up Design

Define operational objectives Identify degrees of freedom Identify primary controlled variables Determine where to set the production rate

Top-Down DesignTop-Down Design

ICheaP-7

16-18 May 2005




“Top-Down” Analysis“Top-Down” Analysis

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

Cost Function

Constraints

Step 1

“Identify operational constraints and preferably a scalar cost function to be minimized”

The energy consumption in terms of aeration power represents the major economic duty in our ASP

JQairQairDeNitrQair

Nitr

Cost FunctionCost Function

ConstraintsConstraints

Operational Constraints: DO concentration Food-to-Microorganisms RatioSludge Retention Time

Effluent Constraints: defined by the legislation requirement for the effluent

DisturbancesDisturbancesIn the TecnoCasic plant an equalization tank is present at the top of the ASP

The influent compositions are the

disturbances which we cannot affect

ICheaP-7

16-18 May 2005





Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

Degrees of Freedom

Step 2

“Identify dynamic and steady-state degrees of freedom (DOF)”

Nm7

Dynamic or Control DOF

Dynamic or Control DOF

Nm5

Optimization DOF

Optimization DOF

Nopt3

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

Nopt, freeNopt Nactive

Nopt, free1

ICheaP-7

16-18 May 2005





Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

• Step 3

Controlled

Variables

Step 3

“Which (primary) variable should we control?”

We first need to control the variables directly related to ensuring optimal

economical operation

dJdWASJL optWASWAS ,

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

The magnitude of the loss will depend on the control strategy used to adjust the WAS flowrate

during operationOpen-Loop Strategies: we want to keep the WAS

flowrate at its setpointClosed-Loop Strategies: we adjust WAS in a feedback fashion in an attempt to keep the

controlled variable at its setpoint

ICheaP-7

16-18 May 2005





Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

• Step 3

Controlled

Variables

Step 3

To identify good candidate controlled variables, one should look for variables that satisfy all of the

following requirements (Skogestad, 2000):

“Which (primary) variable should we control?”

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

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16-18 May 2005




ResultsResults

4600

4800

5000

5200

5400

5600

5800

0 50 100 150 200 250 300 350

Co

st F

un

cti

on

[m

3 /d]

WAS [m3/d]

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

• Step 3

Results

0

5

10

15

20

0 50 100 150 200 250 300 350

SR

T [

d]

WAS [m3/d]

72.5

73

73.5

0 50 100 150 200 250 300 350

Eff

luen

t C

OD

[g

CO

D/m3 ]

WAS [m3/d]

The cost function J goes down as the waste

flowrate increases

ICheaP-7

16-18 May 2005




ResultsResults

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

• Step 3

Results

Positive Deviation

Negative Deviation

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

d1=COD

38628 24589 38650 24758

d2=TKN 33648 26968 33749 26991

d3=TSS 30255 29594 34171 29607The 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




ConclusionsConclusions

Outline

Motivations

Objectives

Plant Description

Process Model

• Bioreactor


Test Motion

Top-Down Analysis

• Step 1

• Step 2

• Step 3

Results

Conclusions

In this work we have considered alternative controlled variables for the TecnoCasic activated sludge process

That is a good starting point to understand how this kind of system can be improve

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

The optimization part has to be implemented and studied for systems with a different

configurationFor 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

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