a java based simulator of activated sludge processes

Mathematics and Computers in Simulation 56 (2001) 333–346

A JAVA based simulator of activated sludge processes

Pär Samuelsson∗, Mats Ekman, Bengt CarlssonDepartment of Systems and Control, Uppsala University, P.O. Box 27, SE-751 03 Uppsala, Sweden

Abstract

A simulator of activated sludge processes in wastewater treatment plants has been implemented in the pro-gramming language JAVA. The simulator can be reached over Internet and operated from a web browser. A novelgraphical user interface is used to operate the simulator and to present the simulation results in real time. Dif-ferent automatic control strategies are implemented in order to illustrate the importance of automatic control inwastewater treatment plants. The simulator has been used for educational purposes both for university studentsand personnel from wastewater treatment plants. A demo version of the simulator is located at the URL address:www.syscon.uu.se/JASS/. © 2001 IMACS. Published by Elsevier Science B.V. All rights reserved.

Keywords: JAVA; Activated sludge process; Wastewater treatment; Automatic control; Simulator

1. Introduction

Modeling and simulation studies are becoming more and more common in the wastewater treatmentarea. Typical application areas include process design, controller design, optimization, forecasting, and re-search. Several simulators and models of the activated sludge process have been developed during the lastdecade [7]. It is, however, fair to conclude that most simulators have not been used very much by plant oper-ators. There are several reasons for this, but one major cause is that the simulators so far have not been veryuser friendly. A new simulator for the activated sludge process, which is believed to be more user friendlythan most existing simulators, has been developed. It has also some other advantages to be discussed.

1. The simulator is developed in the programming language JAVA and can hence be run using an ordinaryInternet browser such as Netscape Communicator or Microsoft Explorer.

2. The simulator is easy to understand and run. For example, to change a flow rate, the user needs just toclick on the corresponding pump symbol, and enter the desired flow rate in a dialog box.

3. A novel graphical user interface (GUI) is used to present process information in a comprehensive way.4. Special concern has been devoted to implement automatic control strategies including dissolved oxy-

gen (DO) control, supervisory DO control and external carbon flow rate control.

∗ Corresponding author.E-mail addresses: [email protected] (P. Samuelsson), [email protected] (M. Ekman), [email protected] (B. Carlsson).

0378-4754/01/$20.00 © 2001 IMACS. Published by Elsevier Science B.V. All rights reserved.PII: S0378-4754(01)00305-6

334 P. Samuelsson et al. / Mathematics and Computers in Simulation 56 (2001) 333–346

Items 1–3 are believed to make the simulator ideal for educational and training purposes. Item 4 maybe a tool to promote the benefits of using automatic control in wastewater treatment plants. With theincreased use of the Internet, the simulator may be also used for distance education.

2. The model and the implementation

The underlying process model used in the simulator is the IAWQ Activated Sludge Model No. 1(ASM1). In Appendix A the ASM1 model is outlined, see [3,4] for a general model description. Threedifferent processes are described by the model, removal of organic matter, nitrification and denitrification.The model contains 13 different components, the behavior of each component is described by a nonlineardifferential equation. ASM1 is probably the most used advanced model for the activated sludge process.When simulating the activated sludge process in a wastewater treatment plant it is also necessary to modela settler since biomass must be settled and recirculated back into the process. The settler unit is modeled asa traditional one-dimensional layer model. The settler is an important part of the activated sludge process.It is used for two purposes: clarification and thickening. In a settler, particulate matter in the water from thebioreactor sink to the bottom of the settler (thickening), and clear water is produced (clarification) and re-moved in the top of the settler. The sludge is recirculated to the bioreactor to maintain a desired solids level.

There exists many different settler models in the literature. In [2] a comparative study where it wasfound that the model by [10] gave the most reliable results was made. This model is therefore used in thesimulator. Some information about the used settler model is given in Appendix A, see [10] for details.

In the default version of the simulator, an activated sludge process with post-denitrification is imple-mented. The implemented process consists of a basin divided into 10 completely mixed compartments,each modeled by ASM1, and a settler. This structure is depicted in Fig. 1. The number of compartmentsis fixed. However, the settings are possible to change in order to simulate, e.g. a pre-denitrifying plant.The settler is modeled with 10 vertical layers. All differential equations are solved with a fourth orderRunge–Kutta method with timestep 0.01 h. Since the simulated activated sludge process (ASP) is dividedinto compartments, the use of an object oriented language, such as JAVA is natural. Each compartment isimplemented as an instance of a class, which makes the structure of the program simple. The settler is aninstance of another class. Another advantage with JAVA is its support for multithreading and graphics.Two threads are implemented in the program, one for handling events and one for running the simulation.

Fig. 1. Schematic layout of the activated sludge process in the simulator.

P. Samuelsson et al. / Mathematics and Computers in Simulation 56 (2001) 333–346 335

Fig. 2. The graphical user interface in the simulator.

The simulator is operated from a GUI where the status of the plant is also shown in real time (Fig. 2).In the lower part of the GUI, the layout of the simulated plant is shown. The aim of this picture is toillustrate some fundamental settings of the model, for instance aerated compartments and active pumps.The picture also contains clickable fields. When such a text field is clicked, new windows, either usedfor presenting data or changing process parameters, appear. To increase clarity, the fields are locatedas near as possible to the corresponding part of the process picture. For instance, text fields containinginformation connected to the settler are displayed close to the settler.

To improve the presentation of simulated process parameters, three bar diagrams are used. Eachbar diagram displays the concentration of a selectable component in influent water, all compartments,the recirculated sludge and the effluent water. Numerical values of the concentrations are also pre-sented. In the GUI an ordinary graph is also used. The concentrations of two components in a se-lectable compartment can be plotted versus time. Most of the functions in the simulator are very easyto learn, see Appendix B for a short user manual. For more detailed information on the programimplementation [8].


3. Control strategies

In the current version of the simulator, three control strategies are implemented.

• DO control. The air flow rate is controlled so that the DO concentration is kept close to some set-point.A standard PID controller which can be tuned from the GUI is used.

• Supervisory DO control. Given a desired ammonium level and a measurement of the ammoniumconcentration in the last aerated compartment, a supervisory controller calculates the set-point for theDO controller above. The controller is a standard PID and can be tuned from the GUI. The controlstrategy works for both pre-and post-denitrifying configurations. In the GUI it is possible to choosebetween standard DO control and the supervisory DO control strategy.

• External carbon flow rate control. The flow rate of external carbon is used to control the nitrate level inthe last anoxic compartment. Here, a feedforward strategy based on a simple mass balance is appliedtogether with a feedback controller. Only the nitrate set-point can be changed from the GUI.

3.1. Supervisory control of the DO flow rates

The nitrification bacteria in the aerobic compartments require a sufficient high concentration of DO inorder to obtain a satisfactory nitrification. On the other hand an excessively high DO level will lead to anunnecessary high energy consumption. In the case when a pre-denitrifying structure is used on the plant,a too high DO level in the internally recirculated water may inhibit the denitrification [6].

Presently the most common DO control strategy is to have a fixed DO set-point. The goal of the DO con-trol is to keep a constant DO level by varying the airflow rate. An interesting alternative to a constant DOset-point is to control the DO set-point from on-line measurements of the ammonium concentration [7].The ammonium concentration in the last aerated compartment determines a time-varying DO set-point.This offers the possibility to design a supervisory DO controller, also known as cascade control (Fig. 3).The supervisory DO controller maintains an ammonium concentration set-point in the last aerated com-partment by varying the DO set-point. If the ammonium concentration is chosen properly, the controllermay give a lower average DO set-point, compared to a constant DO, which saves energy. Other advan-tages may be a better control of effluent ammonium, lower nitrate concentration in the effluent becauseof improved denitrification (due to a lower average DO) and a decreased need of external carbon [5].

In the simulator a supervisory DO controller has been successfully implemented. The PID-parametershave been manually tuned and can be changed from the GUI. The ammonium concentration is mea-sured in the last aerated compartment and the controller works for post-denitrification as well as forpre-denitrification. For information on how to run the supervisory DO controller (See Appendix B).

Fig. 3. A block diagram for the DO set-point controller. The time-varying DO set-point is determined by the ammoniumconcentration in the last aerobic compartment. The superscript (sp) denotes set-point values.


Fig. 4. Comparison between supervisory DO control and DO control with a constant set-point. A load disturbance in the influentammonium is applied at time 50 h. Upper plot: The ammonium concentration in the last aerated zone is displayed for supervisoryDO control (solid line) and DO control with a constant set-point (dashed line). Lower plot: The time-varying DO set-point (solidline) is shown together with the constant DO set-point (dashed line).

Simulation results using post-denitrification with the supervisory DO controller is illustrated in Fig. 4.The same DO set-point is used in all five aerated compartments. The remaining five compartments areanoxic. The set-point of the ammonium concentration is 2 mg/l. The ammonium concentration as wellas the DO set-point are compared with simulation results using a constant DO set-point (2 mg/l). A stepchange in the influent ammonium concentration from 23.5 to 30 mg/l is applied at time 50 h. The figureillustrates that the supervisory DO controller rejects the load disturbance much better than the standardDO controller.

3.2. Control of the external carbon flow rate

3.2.1. A general strategyBiological nitrogen removal in activated sludge processes is dependent on sufficient supplies of readily

biodegradable carbon components for the denitrifying bacterial population. An external carbon sourcemay increase the denitrification rate by compensating for deficiencies in the influent carbon/nitrogen ratio.It is therefore important to control the flow rate of the external carbon source. A too low dosage will leavethe denitrification process carbon constrained and the full capacity for nitrogen removal is not utilized.


A too high carbon dosage is expensive, may cause carbon spill and increases the sludge production. Areasonable strategy for controling an external carbon flow rate is to adjust the flow rate so that the nitratelevel in the last anoxic compartment is kept at a given (low) set-point.

3.2.2. A simple feedforward controllerIn the following, a feedforward controller based on a simple static mass balance will be discussed. A

more complete description is given in [9]. See [1] for a general treatment of bioreactor modeling. The ideais to derive a simple controller that effectively attenuates load disturbances, such as changes in influentflow rate and influent nitrate load.

In steady state, by considering the simplified model of the ASM1 derived in [9], the external carbon flowrate u [m3/h] that corresponds to a certain level of nitrate in the last anoxic compartment can be written

u = Q

CODcar

[1

β(SNO,in − SNO) − (SS,in − SS)

](1)

where SNO [g (N)/m3] is the nitrate concentration in the last anoxic compartment, SS [g (COD)/m3] isthe concentration of readily degradable organic matter in the last anoxic compartment, SNO,in [g (N)/m3]is the nitrate concentration in the influent to the first anoxic compartment and SS,in [g (COD)/m3] is theconcentration of readily degradable organic matter in the influent to the first anoxic compartment. The totalflow rate through the compartment is denoted Q [m3/h], and β is a conversion factor consisting of ASM1stoichiometric constants, see [9]. CODcar [g (COD)/m3] is the COD content of the external carbon source.

The feedforward controller is obtained by replacing SNO with a set-point SspNO and the use of continuously

measured data of concentrations and flow rates. The readily degradable organic matter, SS, is assumed tobe zero. This gives

u(t) = Q(t)

CODcar

[1

β(SNO,in(t) − S

spNO(t)) − SS,in(t)

](2)

As seen in (2) the control signal will react instantly on a change in nitrate and substrate levels in theinfluent water. However, since a simplified model was used for the design, there is no guarantee that thestationary level of nitrate will be equal to the set-point S

spNO. Therefore, the controller is completed with

an integrator in order to get the right static gain from SspNO to SNO. To further increase the performance, a

proportional feedback part is also added. The full control law will then look like

u(t) = Q(t)

CODcar

[1

β(SNO,in(t) − S

spNO(t)) − SS,in(t)

]

+K(SNO(t) − SspNO(t)) + KI

∫ t

t=to

(SNO(τ ) − SspNO(t)) dτ (3)

The control strategy is also depicted in Fig. 5.

3.2.3. IllustrationTo illustrate the performance of the control law (3) some simulations were done. The full ASM1 model

with five aerated and five anoxic compartments depicted in Fig. 5 was simulated. The influent disturbancesshown in Fig. 6 were applied. After 20 h, S∗

S,in is changed from 70 to 10 mg/l, after 60 h Q∗ is changed from3000 to 3500 m3/h, and after 100 h, S∗

NO,in is changed from 1.0 to 7.0 mg/l. The asterisk is used to denote


Fig. 5. Block diagram for describing the carbon flow rate controller. The external carbon source is added in the first anoxiccompartment and the nitrate to be controlled is measured at the end of the last anoxic compartment. The disturbances Q, SNO,in,and SS,in are used in the feedforward control.

Fig. 6. Influent variations used in the simulation.


Fig. 7. Comparison between the feedforward-feedback controller (3), a PI controller and a constant external carbon flow rate.Upper plot: The nitrate concentration in the last anoxic compartment when the feedforward-feedback controller is used isdisplayed (solid line), the nitrate concentration in the last anoxic compartment when the PI controller is used is shown (dashedline). Also, the nitrate concentration for a constant flow rate of external carbon is shown (dashed-dotted line). Lower plot: Theexternal carbon flow rates are shown for the three simulations, respectively.

that these are influent flow concentrations to the plant, and therefore differ from the total influent to thecontrolled anoxic compartments. Finally, after 120 h, the set-point S

spNO is changed from 2.0 to 1.0 mg/l.

Fig. 7 shows the uncontrolled process (a constant external carbon flow rate is applied), the processcontrolled with a PI controller and the process controlled with the feedforward-feedback controller of(3). The same PI parameters were used in the latter cases to make comparisons relevant. As seen fromthe figure, the controller (3) responds rapidly to changes in influent water. A more detailed presentationof the simulations is given in [9].

It has been found from various simulation studies that the fast disturbance rejection obtained fromcontroller (3) is very difficult to achieve with traditional control techniques (such as PID and LQ control)where feedforward is not used.

4. Conclusion

A JAVA based activated sludge process simulator has been presented. Special emphasis has been devotedto create a user friendly platform. Some control strategies have been implemented. The simulator can


be run with traditional DO level control as well as with supervisory DO control. A simple but effectivecontrol strategy for the external carbon flow rate is used in the simulator. The presented feedforwardstrategy has shown to outperform standard control strategies. The simulator has been used for academiceducation and also in courses for personnel from wastewater treatment plants. The simulator is then usedas an integrated teaching tool to illustrate different process alternatives in nitrogen removal using theactivated sludge process.

Acknowledgements

The financial support by the MISTRA program Sustainable Urban Water Management who supportedMats Ekman is gratefully acknowledged.

Appendix A. The simulation model

To evaluate different controllers and control strategies it is important to use a model which realisticallysimulates a true plant. In the simulator, the IAWQ Activated Sludge Model No. 1 (ASM1) by [3] hasbeen used to model each compartment of the implemented process. The settler is modeled with theclarification-thickening model by [1].

For a completely mixed compartment, the following model structure holds for a general component Z

[1]

dZ

dt= RZ(t) + D(t)(Zin(t) − Z(t))

where Zin(t) is the influent concentration of the actual component, Z(t) is the effluent concentra-tion which is equal to the concentration in the compartment under the assumption of completemixing, D(t) is the dilution rate (flow/volume), and RZ(t) denotes the reaction (growth) rate for thecomponent.

Table 1Components included in the model

SNH(t) Soluble ammonium nitrogenSNO(t) Soluble nitrate nitrogenSND(t) Soluble biodegradable organic nitrogenSO(t) Dissolved oxygenSS(t) Soluble readily biodegradable substrateSI(t) Soluble inert organic matterSALK(t) AlkalinityXB,A(t) Active autotrophic biomassXB,H(t) Active heterotrophic biomassXND(t) Particulate biodegradable organic nitrogenXS(t) Slowly biodegradable substrateXI(t) Particulate inert organic matter and products


The different components of the ASM1 model are given in Table 1. According to ASM1 the reactionrates for the components are

RSS = − 1

YHµ̂H

(SS

KS + SS

) (SO

KO,H + SO

)XB,H − 1

YHµ̂H

(SS

KS + SS

) (KO,H

KO,H + SO

)

×(

SNO

KNO + SNO

)ηgXB,H + kh

XS/XB,H

KX + (XS/XB,H)

×((

SO

KO,H + SO

)+ ηh

(KO,H

KO,H + SO

) (SNO

KNO + SNO

))XB,H

RXI = fP(bHXB,H + bAXB,A)

RXS = (1 − fP)(bHXB,H + bAXB,A) − khXS/XB,H

KX + (XS/XB,H)

((SO

KO,H + SO

)

+ηh

(KO,H

KO,H + SO

) (SNO

KNO + SNO

))XB,H

RXB,H = µ̂H

(SS

KS + SS

) (SO

KO,H + SO

)XB,H

+µ̂H

(SS

KS + SS

) (KO,H

KO,H + SO

) (SNO

KNO + SNO

)ηgXB,H − bHXB,H

RXB,A = µ̂A

(SNH

KNH + SNH

) (SO

KO,A + SO

)XB,A − bAXB,A

RSO = −1 − YH

YHµ̂H

(SS

KS + SS

) (SO

KO,H + SO

)XB,H

−4.57 − YA

YAµ̂A

(SNH

KNH + SNH

) (SO

KO,A + SO

)XB,A + KLa(SO,sat − SO)

RSNO = −1 − YH

2.86YHµ̂H

(SS

KS + SS

) (KO,H

KO,H + SO

) (SNO

KNO + SNO

)ηgXB,H

+ 1

YAµ̂A

(SNH

KNH + SNH

) (SO

KO,A + SO

)XB,A

RSNH = −iXBµ̂H

(SS

KS + SS

) (SO

KO,H + SO

)XB,H

−iXBµ̂H

(SS

KS + SS

) (KO,H

KO,H + SO

) (SNO

KNO + SNO

)ηgXB,H

−(

iXB + 1

YA

)µ̂A

(SNH

KNH + SNH

) (SO

KO,A + SO

)XB,A + kaSNDXB,H


RSND = −kaSNDXB,H + XND

XSkh

XS/XB,H

KX + (XS/XB,H)

×((

SO

KO,H + SO

)+ ηh

(KO,H

KO,H + SO

) (SNO

KNO + SNO

))XB,H

RXND = (iXB − fPiXP)(bHXB,H + bAXB,A) − XND

XSkh

XS/XB,H

KX + (XSXB,H)

×((

SO

KO,H + SO

)+ ηh

(KO,H

KO,H + SO

) (SNO

KNO + SNO

))XB,H

The reaction rate of the alkalinity, SALK, has been omitted since it does not affect other components andthe reaction rate of SI since it is inert and does not react. For an explanation of the different processparameters and their default values [3].

The settler is modeled with 10 horizontal layers where each layer is assumed to be completely mixed.The model is based on the solids mass balance around each layer. The solids flux is denoted J and isassumed to depend on settling velocity vS and concentration of a particulate components X according toJ = vS(X)X. The concentration of the particulate components in a specific layer is then found from

dX

dt= 1

h�J (A.1)

where �J is the difference in flux over the layer, see [4] or [10] for a further description of the numericalimplementation of (A.1).

Appendix B. Short users manual

A short overview on how to run the simulator is given.

1. Start an Internet browser, i.e. Netscape Communicator.2. Load the following URL: http://www.syscon.uu.se/JASS/ where a demo version of the simulator is

available.3. Click on the Start button in the GUI.4. To choose a certain component to plot in the bar diagrams, use the menu bar to the left of the

corresponding bar diagram. For instance, to plot the concentration of dissolved oxygen, choose SO.The oxygen concentrations in influent water, all compartments, recirculated sludge and effluent waterare then displayed.

5. In the scrolling graph it is possible to plot the concentrations of two components at the same time.Three menu bars are located under the graph. In the middle menu bar it can be chosen whether toplot concentrations from influent water, any of the compartments, return sludge or the effluent water.Two components may then be selected from the menu bars to the left and to the right.

6. To change a flow rate, click on the corresponding pump symbol in the process image and enter a newvalue in the appearing window. An active pump will be colored green, and an inactive yellow. Alsoan external carbon source (ethanol) can be added.

7. To change ASM1 process parameters, click on the field Constants (upper left corner) in the processimage. A new window where the new parameters may be entered is shown. It is also possible to choose


Fig. 8. The window where the ASM1 process parameters can be changed.

the simulation temperature of the water. To make the text fields writeable, they must be clicked withthe cursor. When a new value has been chosen, push the Ok button in the window. The new valuesare then used in the process, and the window closes. If the button Cancel is chosen, no changes inthe process are done and the window disappears. The window is depicted in Fig. 8.

8. To change concentrations of the components in the influent water, click on the field Influent. A windowsimilar to the one described above appears, and new values can be entered in the same way.

9. By clicking in the field Zone volumes and settler properties, a window appears where the volumesof all compartments can be changed. Here, it is also possible to change the settler parameters such assettler area, settler height and settling velocity.

10. The state of the settler is shown when the field Settler state is clicked. Here the particulate concentrationin the effluent is presented. Also the particulate concentration profile in the settler is shown (Fig. 9).When a sludge overflow occurs, a warning message appears in the window.

11. During simulation, it is possible to choose between running the simulator with a fixed excess sludgeflow or with a fixed sludge age. In the default set-up, the simulator is running with a fixed sludge flow.By clicking in the field Sludge age control and enter a desired sludge age in the appearing window,the simulator will run with a fixed sludge age instead. Clicking on the field Sludge flow control and


Fig. 9. The particulate concentration profile of the settler. Darker color means higher concentration.

enter a desired flow value will now change the mode back, so that the simulator now runs with a fixedexcess sludge flow again.

12. When the field Sludge properties is clicked, a window illustrating the actual sludge age, the excesssludge flow and the sludge concentration of the plant appears.

13. To change the set-points of the dissolved oxygen, go to the panel under the scrolling graph (locatedunder the menu bar DO PID). Here it is chosen in what compartment to change the set-point. Entera value in the text field and then push the Set DOref button. Note that the air bubbles drawn in thecompartment will disappear when DOref = 0 is chosen.

14. When the option DO setpoint controller is chosen from the menu bar DO PID under the graph, it ispossible to run the supervisory DO control strategy described in item 13. The user may then entera set-point for the ammonium concentration in the last aerated compartment. When the button SetSnhref is selected, the supervisory control starts. Once the option DO PID is chosen in the menu baragain, the supervisory control stops.

15. When the option C controller is chosen from the menu bar DO PID under the graph, it is possible toautomatically control the flow rate of the external carbon source. The user may enter the set-point ofnitrate in the last anoxic compartment.

16. The simulated time is presented in two ways. First, it can be seen approximately from the timescaleof the graph, secondly it is presented in a separate text field to the right in the GUI.

17. In case the simulator needs to be reseted, press the button RESET followed by shift + reload in theCommunicator menu.

References

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Sci. Tech. 31 (2) (1995) 193–204.


[3] M. Henze, C.P.L. Grady Jr., W. Gujer, G.v.R. Marais, T. Matsuo, Activated sludge model No. 1, Scientific and TechnicalReport No. 1, 1987. IAWPRC, London.

[4] U. Jeppsson, Modelling aspects of wastewater treatment processes, Ph.D. Thesis, Lund Institute of Technology,Department of Industrial Electrical Engineering and Automation, 1996 (ISBN 91-88934-00-4; CODEN: LUTEDX/(TELE-1010)/1-444/(1996)).

[5] C.-F. Lindberg, Control and estimation strategies applied to the activated sludge process, Ph.D. Thesis, Uppsala University,Systems and Control Group, 1997.

[6] G. Olsson, U. Jeppson, Establishing cause-effect relationships in activated sludge plants, What can be controlled? in:Workshop Modelling, Monitoring and Control of Wastewater Treatment Plants, Med. Fac. Landbouww. Univ. Gent., 1994,pp. 2057–2070.

[7] G. Olsson, B. Newell, Wastewater Treatment Systems, IWA Publishing, 1999.[8] P. Samuelsson, A Java based activated sludge process simulator, Master’s thesis, Systems and Control Group, School of

Engineering, Uppsala University, 1998.[9] P. Samuelsson, B. Carlsson, Feedforward control of the external carbon flow rate in an activated sludge process, in: 1st

World Water Congress of the International Water Association (IWA), Paris, 3–7 July 2000, Vol. 3, pp. 76–83.[10] I. Takács, G.G. Patry, D. Nolasco, A dynamic model of the clarification-thickening process, Water Res. 25 (10) (1991)

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a java based simulator of activated sludge processes

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