Optimization of · PDF fileengineering plants in other industries, ... Specification of requirements for optimization of ... sewage treatment plant is triggered on the basis of a com

White Paper | 27 March 2015

You want to optimize operation of your sewage treatment

plant, for example with respect to process stability, energy

consumption, and compliance with environmental regula-

tions?

Do you want an automation solution that is uniform,

straightforward, and easy to adapt?

This White Paper provides an overview of the available

closed-loop control concepts for this task, and how they

can be implemented transparently and with minimal effort

using the SIMATIC PCS 7 Advanced Process Library.

www.siemens.com/simatic-pcs7/apc

Optimization of Sewage Treatment Plants by Advanced Process Control How can the operation of sewage treatment plants, especially the aeration of biological treatment steps, be optimized using the Advanced Process Control functions of SIMATIC PCS 7?

White paper | Optimization of Sewage Treatment Plants with Advanced Process Control | 27 March 2015

Introduction ................................................................ 3 Basic information on the activated sludge process ........... 3 Challenges in the automation of sewage treatment plants 4 Advanced Process Control for sewage treatment plants ... 4

Optimization of a small sewage treatment plant with

intermittent operation ................................................. 6 Plant description ............................................................. 6 Simulation model ............................................................ 6 Challenges for automation .............................................. 6 Solution concept ............................................................. 7 Simulation results ........................................................... 7 Outlook .......................................................................... 8

Optimization of a large sewage treatment plant in

continuous operation .................................................. 9 Plant description ............................................................. 9 Simulation model ............................................................ 9 Challenges for automation .............................................. 9 Solution concept ........................................................... 10 Simulation results ......................................................... 10 Outlook ........................................................................ 11

Conclusion ................................................................. 12

References ................................................................ 12

Content


Introduction

Basic information on the activated sludge process

Generally, sewage treatment plants based on the activated

sludge process are used for purification of wastewater. A

typical design of these plants is shown in Figure 1 [9.].

Preliminary mechanical purification, consisting of a screen,

grit chamber and preliminary sedimentation tank, initially

removes coarse contaminants and substances that deposit

on the bottom.

The preliminarily purified wastewater then goes to the

activated sludge tank where it is purified biologically by

the activated sludge process. The activated sludge is sepa-

rated from the purified water by a settling process in the

secondary sedimentation tank. Most of the activated

sludge is then fed back to the activated sludge tank. The

purified water is usually introduced into rivers and lakes.

Closed-loop control of the biological operations in the

activated sludge tank poses the greatest challenge for

sewage treatment plant automation.

The materials in the wastewater are biologically degraded

using activated sludge in the respective tanks of a sewage

treatment plant. Aerobic bacteria (bacteria requiring oxy-

gen) break down the carbon compounds primarily into

carbon dioxide and biomass. As part of the nitrification

process, nitrogen from organic compounds, which exists

predominantly in the form of inorganic ammonium NH4, is

split off by other bacteria first as ammonia (NH3) and then

oxidized with oxygen into nitrite and then nitrate.

NH3 + 2 O2 → NO3− + H+ + H2O.

The nitrosomonas and nitrobacter bacteria involved in

nitrification grow much more slowly than the hetero-

trophic bacteria involved in carbon elimination. Because

“Because the sewage treatment plant dis-charges directly into the environment, the opera-tor is responsible for complying with legal limits in the purified wastewater."

Figure 1: Typical process diagram of a medium-sized sewage treatment plant


they also need more oxygen and are more sensitive to

temperature fluctuations, they must be promoted in a

targeted manner.

The nitrate produced during nitrification is reduced to

elemental nitrogen under anoxic conditions, that is, in the

absence of molecular oxygen:

2 NO3− + 12 H+ + 10 e− → N2 + 6 H2O.

For this the bacteria must convert their metabolism from

oxygen respiration to nitrate respiration, which has to be

forced through a lack of oxygen and is referred to as deni-

trification. This also requires a sufficient number of easily

degradable carbon compounds as electron donors.

A variety of activated sludge process techniques are avail-

able, including upstream, simultaneous, and intermittent

denitrification. In upstream denitrification, an initial acti-

vated sludge tank is operated under anoxic conditions, and

the sludge/wastewater mixture is pumped back from the

aerated tanks, which contain a high oxygen content. This

is also shown in Figure 1. As a result, adequate carbon is

available from the influent to the first tank and nitrate

from the recirculation flow. In intermediate denitrification,

anoxic conditions are produced temporarily in a single

tank by phased shutdown of the aeration.

Both an intermittently operated sewage treatment plant

and one with upstream denitrification will be presented as

example plants.

Challenges in the automation of sewage treatment plants

The largely automated operation of sewage treatment

plants is considered state of the art. Compared to process

engineering plants in other industries, such as the chemi-

cal industry, a sewage treatment plant has fewer sensors

and control loops. Nevertheless, sewage treatment plant

automation has its own special challenges:

The sewage treatment plant discharges directly

into receiving waters. The sewage treatment

plant operator is therefore liable for complying

with legal limits in purified wastewater, such as

limits for ammonia nitrogen NH4-N, total nitro-

gen, chemical oxygen demand (COD), and phos-

phate.

The biological processes for purification of the

wastewater using different bacteria in the nitrifi-

cation and denitrification sub-steps are complex

and not easily be modeled [1.]. Careful attention

must be paid that the bacteria, as living beings,

are provided with the right environmental condi-

tions so that they fulfill exactly the desired task

with their metabolic processes.

Many of the variables important for closed-loop

control, especially concentrations, are not meas-

ured online but are available only at longer inter-

vals as lab samples.

The influent to a sewage treatment plant is sub-

ject to strong fluctuations in terms of flow rate

and components. This is due to weather-related

and seasonal fluctuations as well as the behavior

of the high numbers of private individuals and in-

dustrial companies that discharge wastewater to

the sewer network.

Sewage treatment plants and, in particular, the

aeration of biological purification steps represent

the largest communal energy consumer in many

cities and municipalities. Measures that reduce

energy consumption can therefore pay for them-

selves after a short amount of time.

Based on the typical size of mainly municipal

sewage treatment plants, engineers with relevant

know-how about wastewater-specific, biotechno-

logical issues are usually available on-site, but not

control engineers. That is why any control solu-

tion must have a clear and transparent structure

so that it can be operated and maintained by the

available personnel.

Advanced Process Control for sewage treatment plants

The use of I&C (instrumentation and control) technology

in sewage treatment plants generally has the following

objectives:

Improvement of purification performance for

compliance with discharge limits.

Minimization of operating costs, especially ener-

gy costs.

Saving of investment costs through optimal use

of existing infrastructures.

Sophisticated control engineering methods, which have

become known under the keyword Advanced Process Con-

trol (APC) in other industry sectors such as the chemical

industry and oil refining industry, offer potential for opti-

mization of process control in the water/wastewater indus-

try as well. Ever since these methods have been seamlessly

integrated into modern distributed control systems such as

SIMATIC PCS 7 [2.] and made available at low cost as

standard software blocks [3.], nothing more stands in the

way of their successful application in sewage treatment

plants.

Model Predictive Control (MPC) is particularly attractive in

this context. MPC allows "predictive operation" of the plant

because it takes into consideration both the physi-

cal/chemical/biological interactions between different

variables and measurable disturbance effects, for example

from the influent. MPC is integrated as a standard function

block in the SIMATIC PCS 7 Advanced Process Library with

the name ModPreCon.

This paper shows, on the basis of specific real world ex-

amples, how this optimization potential can be tapped.


The case studies are pilot projects implemented with a

uniform approach:

Configuration and parameter assignment of a

simulation model of the specific sewage treat-

ment plant, build on an industry-specific library

of plant components [4.].

Simulation study on the current state of automa-

tion (baseline).

Specification of requirements for optimization of

process control.

Design and configuration of an APC solution for

the existing plant type through a combination of

standard function blocks and use of associated

software tools for computer-aided commissioning

of closed-loop control functions.

Benchmarking simulation in order to quantify the

improvement potential of the APC solution.

The goal of the pilot projects is to develop a generalizable

APC solution for widespread plant types. The studies nec-

essary for this can only be performed using a detailed

simulation model. In the meantime, extensive field experi-

ence exists so that APC solutions can be created for com-

parable sewage treatment plants without new simulation

studies. The fact that the laborious modeling step is elimi-

nated not only saves costs but also valuable time until

commissioning of the new closed-loop control concept at

a new sewage treatment plant.


Optimization of a small sewage treatment plant with intermittent operation

Plant description

The example to be examined is a small sewage treatment

plant with intermittent denitrification. Figure 2 shows the

structure of the sewage treatment plant. The activated

sludge tank is operated alternately for nitrification and

denitrification by switching the aeration on and off.

The timing of the phase changeover is based on a meas-

urement of the ammonium and nitrate concentrations

(hereinafter always indicated as ammonium or nitrate

nitrogen concentration) in the activated sludge tank. The

denitrification phase is ended when the nitrate concentra-

tion falls below a specified value. The aeration is then

switched on in order for the ammonium not treated in the

denitrification phase to be degraded again in the nitrifica-

tion phase. This is ended when the ammonium concentra-

tion falls below a specified level.

Figure 2: Flow diagram of the sewage treatment plant

Simulation model

A model is created for the sewage treatment plant in a

Siemens internal tool for simulation of biological and

chemical process technology. For modeling of the biologi-

cal processes in the activated sludge tank, the "Activated

Sludge Model No. 1" (ASM1) is used [5.]. The modeling of

the secondary sedimentation tank is based on the "Sec-

ondary settler" model from "Benchmark Simulation Model

No. 1" (BSM1) [6.].

The simulation, which is compared with measured data of

the real sewage treatment plant, describes the essential

dynamic processes of the sewage treatment plant effec-

tively.

Challenges for automation

The current control solution, which is referred to hereinaf-

ter as conventional closed-loop control, consists on the

one hand of the changeover of the two phases of the

activated sludge tank. The changeover between nitrifica-

"Sewage treatment plants and, in particular, the aera-tion of biological purifi-cation steps represents the largest communal energy consumer in many cities."


tion and denitrification in the intermittently operated

sewage treatment plant is triggered on the basis of a com-

bination ammonium/nitrate probe. The aeration is

switched off to end the nitrification phase as soon as

enough ammonium has been broken down. This is the

case when the ammonium concentration has dropped

below a threshold value of 0.5 mg/L. Denitrification is

ended as soon as the nitrate concentration has dropped

below a threshold value of 8 mg/L indicating that enough

nitrate has been broken down.

The changeover logic is supplemented with minimum and

maximum phase durations to prevent excessively fast

changeovers and excessively long dwell times.

The aeration control loop during the nitrification phase

represents the second part of the conventional closed-loop

control. A constant dissolved oxygen (DO) setpoint is spec-

ified and is controlled by a PI controller via a blower.

The typical duration of the nitrification phase, however, is

not always sufficient for the oxygen controller to reach a

steady-state oxygen concentration by varying the blower

speed. At the same time, the blower speed at the start of

the nitrification phase is very high, which uses a signifi-

cant amount of energy.

The influent flow rate has a considerable effect on the

dynamic processes in the sewage treatment plant. Howev-

er, the current automation cannot react directly to these

fluctuations because the influent flow rate is not taken

into account in conventional closed-loop control. A signifi-

cant variation in the concentrations in the activated sludge

tank must occur before the phase durations will be modi-

fied and the oxygen controller will adjust the blower speed

as needed.

Solution concept

The oxygen concentration in the nitrification phase is only

an auxiliary controlled variable for providing the appropri-

ate environmental conditions for aerobic metabolism of

bacteria. An oxygen concentration that is constant via an

extended time period is not always achievable, but it is

also not really necessary from process point of view.

With the new concept, therefore, the blower speed is con-

trolled directly by the process variable of primary interest,

that is, the ammonium concentration. The goal is to drop

ammonium from a measured value at the start of the

nitrification phase to a specified target value within a

specified time in order to end the nitrification phase.

Moreover, the influent flow rate is interpreted as a meas-

urable disturbance variable. If the effects on the processes

in the nitrification phase are known, the controller can

adjust the aeration in time to prevent, or at least reduce,

the negative effects of influent fluctuation.

The ModPreCon MPC function block is ideally suited for the

described tasks. It includes a setpoint prefilter that speci-

fies the desired transition time of the ammonium concen-

tration.

Due to the above-described limitations of the PI controller,

the MPC is not cascaded with the existing oxygen control-

ler. Accordingly, the MPC uses the blower speed directly as

the manipulated value in order to control the ammonium

concentration while taking into account influent as a

measurable disturbance variable.

The conventional PI oxygen controller used to date is kept

as a backup solution. The passive controller in each case is

run in tracking mode so that a bumpless changeover is

ensured at any time.

The MPC Configurator included as a standard feature in

SIMATIC PCS 7 can be used for parameter assignment of

the MPC block. Measured data of the manipulated, dis-

turbance, and controlled variables must be recorded in

which excitations of the manipulated and disturbance

variables occur. Since this example includes only one ma-

nipulated variable (blower speed) and one disturbance

variable (influent), the data recording can be carried out in

parallel with normal operation. The nitrification phase may

need to be extended, but this has no negative effects on

the biological processes in the sewage treatment plant.

The MPC Configurator provides automatic MPC design,

using a few transparent parameters for adjustment of the

dynamic behavior.

Simulation results

The conventional closed-loop control implemented in

SIMATIC PCS 7 and the automation with MPC are connect-

ed to the simulator. A time period of 25 hours is simulated,

which is shown in Figure 3. Since there is no real influent

data for the plant, a synthetic influent profile is used.

The conventional closed-loop control is shown in red and

the MPC solution in blue. The durations of the individual

phases can differ with the two control methods, so the

phases are shifted in the figure.

The simulation starts in a non-aerated denitrification

phase. Once enough nitrate has been broken down, the

aeration starts. The curves now differ because the two

controllers specify different blower speeds.

The purification performance is comparable in the two

control methods because the ammonium and nitrate con-

centrations remain the same on average.

It can be clearly seen that the MPC solution provides a

lower oxygen concentration in the activated sludge tank

than conventional closed-loop control. Because the blower

often runs at maximum speed with the conventional

closed-loop control, this means significant energy savings,

amounting to 33% in the time period examined.


Figure 3: Simulation in intermittent operation with influent fluctuations. Red: conventional closed-loop control, blue: MPC; ammonium concentration (NH4), nitrate concentration (NO3), dissolved oxygen concen-tration (O2) in the nitrification tanks, blower speed (n), and influent flow rate (Feed)

The increases of the influent flow rates after 2.5 and 14.5

hours trigger an increase in the blower speed by means of

the closed-loop ammonium control and feedforward con-

trol of the MPC. The behavior of the MPC control loop thus

approaches that of the conventional oxygen control loop,

which blows at maximum speed nearly all the time any-

way. Of interest, in contrast, are the decreases in the in-

fluent. In the case of small influent flows, the MPC throt-

tles back the blower speed considerably, which explains

the significant energy savings.

Outlook

The simulation results are promising. Although the quality

of the effluent values is the same with the new MPC solu-

tion as when using conventional closed-loop control, sig-

nificant energy savings for aeration are possible. 0 5 10 15 20 250

1

2

3

Time/h

NH

4/m

gL

-1

0 5 10 15 20 250

2

4

6

Time/h

NO

3/m

gL

-1

0 5 10 15 20 250

0.5

1

1.5

Time/h

O2/m

gL

-1

0 5 10 15 20 250

50

100

Time/h

n/%

0 5 10 15 20 2520

40

60

80

Time/h

Feed/m

3h

-1

classic

MPC


Optimization of a large sewage treatment plant in continuous operation

Plant description

The second example to be examined is a large sewage

treatment plant whose basic structure is shown in Figure

4. After the inflow and preliminary sedimentation, the

wastewater initially goes to the upstream denitrification.

Nitrification then takes place in the aerated tank. Before

secondary sedimentation, a portion of the water is

pumped back into the denitrification tank as an internal

recirculation stream.

Simulation model

A simulation model exists for this sewage treatment plant

based on Matlab/Simulink and the SIMBA library [7.]. The

model simulates using a variable cycle time that can be

reduced down to 5 s. The simulation model operates with

real measured influent flow rates and concentrations as

well as temperatures in a time period up to 1.5 years and

reflects the real plant characteristics during this time peri-

od very effectively. The existing automation is also inte-

grated into the simulation model.

Figure 4: Flow diagram of the sewage treatment plant

Challenges for automation

Two independent manipulated variables are available for

closed-loop control in this sewage treatment plant: the

aeration of the nitrification tank and the recirculation rate.

In the conventional closed-loop control of the automation

to date, the aeration is manipulated by a PI controller that

controls an oxygen concentration corresponding to the

constant oxygen setpoint in the nitrification tank. The

recirculation is controlled using a characteristic curve

based on the nitrate concentration.

"Any control solution must have a clear and transparent structure so that it can be operated and maintained by the available personnel."


The conventional closed-loop control cannot influence the

effluent concentrations directly. Moreover, the operation

of the two controllers is not coordinated even though the

controlled systems are strongly interacting from the pro-

cess engineering perspective.

Because the wastewater composition is largely constant, it

is the influent flow rate above all that affects the plant

behavior. The existing closed-loop control, however, can

only react to fluctuations after the oxygen concentration

or nitrate concentration move away from their setpoints.

Solution concept

The most important criterion for automation of sewage

treatment plants is compliance with legal effluent limits.

The measured values of the ammonium and nitrate con-

centrations in the discharge of the secondary sedimenta-

tion tank are thus selected as controlled variables. Closed-

loop control on the basis of concentration measurements

in the nitrification tank would also be possible. The neces-

sary manipulated variables of the MPC are the oxygen

concentration setpoint in the nitrification tank, which is

controlled by a slave control loop with a PI oxygen control-

ler, and the recirculation rate. The influent flow rate is also

measured and used for dynamic feedforward control.

Unlike in the first plant example, the oxygen controller can

continue to be used here and is employed as a slave con-

troller of a cascade structure because it quickly and reliably

achieves the desired oxygen concentration.

This results in a multi-variable problem for closed-loop

control in which both manipulated variables affect both

controlled variables. The disturbance variable also affects

both controlled variables. A satisfactory solution to this

control engineering problem is not possible with single-

loop controllers, such as PI controllers. A multi-variable

controller must therefore be used. The ModPreCon func-

tion block from the Advanced Process Library in SIMATIC

PCS 7 is ideally suitable for this task and is therefore con-

nected to the existing Matlab/Simulink simulation of the

sewage treatment plant.

To assign the MPC parameters, suitable training data must

first be recorded, just like in the first sewage treatment

plant example. The manipulated and disturbance variables

are individually excited in a targeted manner for this. A

mathematical model of the plant behavior can be obtained

from this data with the MPC Configurator and then used to

assign the MPC function block parameters.

Simulation results

In an idealized simulation in which all external influencing

factors are kept constant, it can be effectively proven that

the MPC is able to directly control the multi-variable prob-

lem presented. Targeted excitations of the setpoints and

the disturbance variable for this are shown in Figure 5.

After one day, the setpoint of the ammonium concentra-

tion is modified, whereby all setpoints are chosen arbitrari-

ly. The MPC reacts with an adjustment of the oxygen con-

centration and the recirculation, which leads to fast track-

ing of the ammonium concentration setpoint. At the same

time, the nitrate concentration is hardly affected.

After three days, the setpoint of the nitrate concentration

is modified, whereupon the MPC again adjusts the oxygen

concentration and the recirculation. Within one day, the

new nitrate concentration reaches a steady-state value

and fluctuation of the ammonium concentration is cor-

rected.

Figure 5: Idealized simulation with constant external influencing factors. Ammonium concentration (NH4), nitrate concentration (NO3) in the discharge from the secondary sedimentation tank, blue: setpoint, red: actual value; dissolved oxygen concentration setpoint in the nitrification tank (O2), recirculation rate (Reci.) and influent flow rate (Feed)

The influent flow rate is changed on the fifth day. The MPC

uses both manipulated variables again to correct the am-

0 1 2 3 4 5 6 7 80.4

0.6

0.8

Time/Days

NH

4/m

gL-1

0 1 2 3 4 5 6 7 87.5

8

8.5

Time/Days

NO

3/m

gL

-1

0 1 2 3 4 5 6 7 81.5

2

2.5

3

Time/Days

O2/m

gL

-1

0 1 2 3 4 5 6 7 82

2.5

3

3.5x 10

5

Time/Days

Reci./m

3D

ay

-1

0 1 2 3 4 5 6 7 81.2

1.3

1.4

1.5x 10

5

Time/Days

Feed/m

3D

ay

-1


monium and nitrate concentration errors and achieves a

steady-state within two days.

The suitability of the MPC for closed-loop control of the

sewage treatment plant under real environmental condi-

tions is demonstrated by a long-term simulation over 240

days, which is summarized in Figure 6. Here, the MPC is

designed in such a way that it only intervenes when high

concentrations in the controlled variables threaten to

violate the legal limits. The rest of the time, it strives to

reach an oxygen setpoint of 1.3 mg/L, which is less than

the constant setpoint of 1.5 mg/L of the conventional

closed-loop control.

Overall, it can be clearly seen that the ammonium and

nitrate concentration curves are largely similar. However,

the MPC reacts to higher concentrations with active atten-

uation so that the maximum pollution loads are reduced.

The very low inflow rate near day 50 results in a high ni-

trate concentration in the conventional closed-loop con-

trol. The effect of the disturbance variable compensation

via MPC is clearly visible: it reduces the nitrate concentra-

tion significantly.

Because the MPC commands a lower oxygen setpoint in

the nitrification tank on average, less energy is used for

aeration of the nitrification tank compared to conventional

closed-loop control. This amounts to an energy saving of

5.4% over the examined time period of 240 days.

Outlook

The simulation results show that the MPC can also reliably

control a large and complex sewage treatment plant. In

doing so, not only are the effluent concentrations im-

proved but a significant amount of energy is saved for

aeration. Overall, the MPC enables straightforward, trans-

parent, and uniform automation of the sewage treatment

plant.

Up to now, only the influent flow rate has been integrated

in the automation concept using MPC. Because the tem-

perature, in particular, has a large, but identifiable effect

on plant behavior, additional studies will analyze how

temperature can be integrated into the closed-loop control

concept.

Figure 6: Long-term simulation. Red: conventional closed-loop control, blue: MPC; ammonium concentra-tion (NH4), nitrate concentration (NO3) in the dis-charge from the secondary sedimentation tank, dis-solved oxygen concentration setpoint in the nitrifica-tion tank (O2), recirculation rate (Reci.), influent flow rate (Feed), and temperature (Temp.):

0 50 100 150 2000

10

20

Time/Days

NH

4/m

gL

-1

classic control

MPC

0 50 100 150 2000

10

20

30

Time/Days

NO

3/m

gL

-1

0 50 100 150 2000

2

4

6

Time/Days

O2/m

gL

-1

0 50 100 150 2000

5

10x 10

5

Time/Days

Reci./m

3D

ay

-1

0 50 100 150 2000

2

4x 10

5

Time/Days

Feed/m

3D

ay

-1

0 50 100 150 2000

10

20

30

Time/Days

Tem

p./

°C


Conclusion The simulation of two sewage treatment plants – one

small plant with intermittent denitrification and one large

plant in continuous operation – demonstrates clear ad-

vantages of automation with the Advanced Process Library

of SIMATIC PCS 7. Compliance with discharge limits is

ensured or even improved. Moreover, potential for signifi-

cant energy savings is revealed. In addition, the MPC block

allows a uniform and straightforward automation solution

that can react to fluctuations in the inflow rate without

user intervention. Implementation and parameter assign-

ment of the MPC is managed very easily through the use

of standard function blocks and off-the-shelf commission-

ing tools.

As a result, automation with SIMATIC PCS 7 contributes

significantly to "operational excellence" [8.] and supports

efficient operation of water and wastewater treatment

plants with functions for Advanced Process Control inte-

grated into the distributed control system.

References [1.] Hansen, J.: Der Einsatz von Fuzzy Control für Re-

gelungsaufgaben im Bereich der Nährstoffelimi-

nation in kommunalen Kläranlagen. Dissertation

Kaiserslautern University , Department of Urban

Water Management, 1997.

[2.] Siemens AG, SIMATIC PCS 7.

www.siemens.com/simatic-pcs7

[3.] Siemens AG, Industry Sector, Industrial Automa-

tion: White Paper "How to Improve the Perfor-

mance of your Plant Using the Appropriate Tools

of SIMATIC PCS 7 APC-Portfolio?"

http://www.automation.siemens.com/w2/efiles/pc

s7/support/marktstudien/WP_PCS7_APC_EN.pdf,

2008.

[4.] Siemens AG, Water Library.

www.siemens.com/water/waterlibrary

[5.] Jeppsson, U.: A general description of the Activat-

ed Sludge Model No. 1 (ASM1), Lund Institute of

Technology, Lund, 1996.

[6.] Alex, J., L. Benedetti, J. Copp, K. Gernaey, U.

Jeppsson, I. Nopens, M.-N. Pons, L. Rieger, C.

Rosen, J. Steyer, P. Vanrolleghem and S. Winkler:

Benchmark Simulation Model no. 1 (BSM1), Dept.

of Industrial Electrical Engineering and Automa-

tion, Lund University, 2008.

[7.] ifak system GmbH, SIMBA, http://www.ifak-

system.com/umwelttechnik/simba/

[8.] Siemens AG, Industry Sector, Industrial Automa-

tion: White Paper "Which contributions to "opera-

tional excellence" and efficient operation of pro-

cess plants can be expected from automation

with SIMATIC PCS 7?".

http://w3.siemens.com/mcms/process-control-

systems/SiteCollectionDocuments/efiles/pcs7/

support/pdf/76/WP_Op-Eff-PCS7_EN.pdf, 2011.

[9.] Pfeiffer, B-M., Labisch, D., Grieb, H., Brandstetter,

V., Wehrstedt, J.C., Pirsing, A.: Optimierungspo-

tentiale bei Kläranlagen durch den Einsatz mo-

dellbasierter prädiktiver Regelungen. Automation

2015, Baden-Baden. Conference CD, VDI-Verlag,

Düsseldorf.

Siemens AG

Process Industries and Drives

Water and Wastewater

PD PA AE W&W 3

P.O. Box 4848

90026 Nuremberg, Germany

www.siemens.com

All rights reserved. All trademarks used

are owned by Siemens or their respective owners.

© Siemens AG 2015

http://www.siemens.com/simatic-pcs7

http://www.automation.siemens.com/w2/efiles/pcs7/support/marktstudien/WP_PCS7_APC_DE.pdf

http://www.automation.siemens.com/w2/efiles/pcs7/support/marktstudien/WP_PCS7_APC_DE.pdf

http://www.siemens.de/wasser/wasserbibliothek

http://www.ifak-system.com/umwelttechnik/simba/

http://www.ifak-system.com/umwelttechnik/simba/

http://w3.siemens.com/mcms/process-control-systems/SiteCollectionDocuments/efiles/pcs7/%20support/pdf/00/WP_Op-Eff-PCS7_DE.pdf

