mÖderl michael friedrich - universität innsbruck · mÖderl michael friedrich sustainable waste...

MÖDERL Michael Friedrich

SUSTAINABLE WASTE WATER TREATMENT BY MEANS OF URINE SEPARATION

DISCHARGE STRATEGIES OF CONTROLLED URINE FLOW FROM DOMESTIC SEWAGE

DIPLOMARBEIT

EINGEREICHT AN DER LEOPOLD-FRANZENS-UNIVERSITÄT INNSBRUCK

FAKULTÄT FÜR BAUINGENIEURWISSENSCHAFTEN

zur Erlangung des akademischen Grades

“DIPLOM-INGENIEUR”

Beurteiler:

Univ. Prof. Dipl. Ing. Dr. techn. Wolfgang Rauch

Institut für Infrastruktur

Innsbruck, Januar 2006

Chapter 1 Introduction

i

TABLE OF CONTENTS

1. Introduction....................................................................................................................... 1

1.1. “Classical” Urine separation ...................................................................................... 1

1.2. Sanitary facilities for the urine separation.................................................................. 2

1.3. Alternative utilisation of urine separation.................................................................. 3

1.4. Extension of previous work........................................................................................ 4

2. Materials and methods ..................................................................................................... 5

2.1. Software which is used for numerical modelling....................................................... 5

2.1.1. CITY DRAIN modelling environment .............................................................. 5

2.1.2. Example blocks from the CITY DRAIN software............................................. 6

2.2. Stochastic modelling of the urine production ............................................................ 8

2.2.1. Mathematical description of urine production ................................................... 8

2.2.2. Implementation within an urban drainage model............................................. 12

2.3. Tested control strategies........................................................................................... 15

2.3.1. Basic control options (BCO) ............................................................................ 16

2.3.2. Interceptive control options (ICO) ................................................................... 17

2.3.3. Urine control options (UCO)............................................................................ 17

2.4. Quality criterions of urine tank control options ....................................................... 20

2.4.1. Averaging of the ammonium load at WWTP (CR 1)....................................... 21

2.4.2. Ammonium overflow load reduction at CSO (CR2 – Emission based) .......... 21

2.4.3. In stream ammonium peak concentration (CR3 – Immission based) .............. 25

2.4.4. Application of evaluation criterions................................................................. 26

2.4.5. Optimization the basic control options (BCO)................................................. 27

3. Test scenario A - Virtual catchment.............................................................................. 28

4. Test scenario B - Case study Vils-Reutte ...................................................................... 29

4.1. Data collection.......................................................................................................... 31

4.1.1. Sub catchment (CA) ......................................................................................... 31

4.1.2. Transport sewers .............................................................................................. 35

4.1.3. Combined sewer overflow structures (CSO) ................................................... 36

4.1.4. Pumping stations (PS) ...................................................................................... 36

4.1.5. Rain data (r)...................................................................................................... 37

4.1.6. Flow data .......................................................................................................... 37


ii

4.2. Model calibration ..................................................................................................... 40

4.2.1. Indicators for calibration quality ...................................................................... 42

4.2.2. Calibrating the dry weather flow (DWF) ......................................................... 43

4.2.3. Calibrating the wet weather flow (WWF)........................................................ 44

4.2.4. Calibrating the ammonium pollutograph ......................................................... 48

5. Results – Evaluation of control strategies..................................................................... 49

5.1. Test scenario A - Virtual catchment......................................................................... 49

5.1.1. Basic control options (BCO) at DWF .............................................................. 49

5.1.2. Urine control option (UCO) at WWF .............................................................. 50

5.1.3. Choosing best control strategy (Combination of CR1 and CR2)..................... 52

5.2. Test scenario B - Case study Vils............................................................................. 54

5.2.1. Base control options (BCO) at DWF ............................................................... 54

5.2.2. Urine control option (UCO) at WWF .............................................................. 55

5.2.3. Choosing the best control strategy (Combination of CR1 and CR2)............... 59

5.3. Comparison of control strategies at scenarios A and B ........................................... 61

5.4. Economical considerations....................................................................................... 62

5.4.1. Calculation of depreciation implementation costs ........................................... 63

5.4.2. Costs of initial construction.............................................................................. 64

5.4.3. Costs of implementation based on a support model......................................... 64

5.4.4. Comparison of implementation costs ............................................................... 65

6. Summary and Conclusions............................................................................................. 66

7. Literature......................................................................................................................... 68


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TABLE OF FIGURES

Figure 1: Urine separation toilet adapted for temporally urine storage (Rauch et al., 2002)..... 2

Figure 2: Screenshot of CITY DRAIN library (Achleitner et al., 2006) ................................... 6

Figure 3: Used gamma distributions .......................................................................................... 9

Figure 4: Normalized ammonium pollutograph (time step size 300 s).................................... 11

Figure 5: Subroutines of the GenCon block............................................................................. 12

Figure 6: Mask of the GenCon block ....................................................................................... 13

Figure 7: Block mask of GenCon block for static input parameters........................................ 14

Figure 8: Control strategy options............................................................................................ 16

Figure 9: Fixed time control in connection with interceptive control options ......................... 18

Figure 10: Random control in connection with interceptive control options with r (rain), QDR

(effluent flow of CSO structure) and prob (interceptive probability) ................ 19

Figure 11: Random PDF control in connection with interceptive control options with r (rain),

QDR (effluent flow of CSO structure) and prob (the interceptive probability) ... 20

Figure 12: Event based maximum gliding means for ammonium mass flow (means and

maximums of UCO) ........................................................................................... 23

Figure 13: Linear regression analysis of event based results plotted against the baseline

scenario............................................................................................................... 24

Figure 14: Scheme to evaluate Cj,N .......................................................................................... 26

Figure 15: Criterion I and ammonium pollutograph (WWTP inflow) of calibrated BCO....... 27

Figure 16: Test scenario „virtual catchment“........................................................................... 28

Figure 17: Map of the case study Vils-Reutte.......................................................................... 29

Figure 18: Schematic map of the catchment Vils-Reutte......................................................... 30

Figure 19: Scheme of theoretical flow routes within a sub catchment .................................... 32

Figure 20: Scheme of flow routes between sub catchments .................................................... 35

Figure 21: DWF of main sewer Reutte and Vils at workdays and weekends .......................... 38

Figure 22: River gauge stations of Vils and Lech.................................................................... 38

Figure 23: River scheme and inflow points ............................................................................. 39

Figure 24: Scenario B – City Drain model of catchment Vils Reutte...................................... 41


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Figure 25: Simulated and measured DWF hydrographs main sewer Reutte ........................... 43

Figure 26: Simulated and measured DWF hydrographs main sewer Vils ............................... 44

Figure 27: QMAX and volume ratio of main sewer Reutte ........................................................ 45

Figure 28: QMAX and volume ratio of main sewer Vils............................................................ 46

Figure 29: WWF sample of Rain event main sewer Reutte ..................................................... 47

Figure 30: WWF sample of Rain event main sewer Vils......................................................... 47

Figure 31: Ammonium pollutograph of a sub catchment and the WWTP inflow ................... 48

Figure 32: Criterion of averaging; ammonium pollutograph (DWF, virtual catchment)......... 49

Figure 33: Criterion I for Scenario A (virtual catchment-WWF) ............................................ 50

Figure 34: Criterion II for Scenario A (virtual catchment, WWF) .......................................... 51

Figure 35: Criterion I+II (uniform weighted) for scenario A (virtual catchment, WWF) ....... 52

Figure 36: Efficiency of control strategy UCO1-1 vs. degree of implementation................... 53

Figure 37: Criterion of averaging; ammonium pollutograph (DWF, case study Vils) ............ 54

Figure 38: Averaging of ammonium load pollutograph at WWTP (WWF) ............................ 55

Figure 39: Evaluation of UCO regarding criterion II............................................................... 56

Figure 40: Evaluation points of CR III..................................................................................... 57

Figure 41: Comparison of immission and emission based criterion ........................................ 58

Figure 42: Uniform weighted criterion based on test scenario B............................................. 59

Figure 43: Efficiency of control strategy UCO1-1 vs. degree of implementation................... 60

Figure 44: Comparison of criterions II for scenarios A and scenario B................................... 61

Figure 45: Comparison of implementation costs ..................................................................... 65


v

TABLE OF TABLES

Table 1: Single indicators used in criterion II .......................................................................... 24

Table 2: Single indicators used in criterion III......................................................................... 25

Table 3: Data requirements for modelling ............................................................................... 31

Table 4: Data of sub-catchments in the drainage Area Vils-Reutte ......................................... 34

Table 5: Lengths (l) and flow time (tf) of transport sewers between sub catchments.............. 35

Table 6: CSO data .................................................................................................................... 36

Table 7: Pumping station data.................................................................................................. 36

Table 8: River data ................................................................................................................... 39

Table 9: Rates of support model .............................................................................................. 64

Table 10: Cumulative costs of different implementations ....................................................... 65


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LIST OF ABBREVIATIONS

CSO Combined sewer overflow WWTP Wastewater treatment plant CD4WC Cost-effective development of urban wastewater systems for water framework

Directive compliance (research project supported by the European Commission under the fifth framework programme)

DWF Dry weather flow PE Population equivalent CSS Combined sewer system SSS Separate sewer system PS Pumping station CA Catchment (or Sub-Catchment) tf Flow time in the catchment A Catchment (or sub catchement) area (total area) f Runoff coefficient AEFF Effecitive flow contributing area (AEFF=A*f) hV,I Initial loss hV,D Permanent loss (applied in dry period only) QDR Effluent flow at CSO QDR,max Maximum effluent flow at CSO BCO Basic control option (applied during dry weather flow) ICO Interceptive control option (applied during rain period) UCO Urine control option AWV-Vils Abwasserverband Vils-Reutte und Umgebung CR I Criterion of averaging CR II Emission based criterion CR III Immission based criterion PDF Probability density function


vii

DECLARATION

This diploma thesis

Sustainable waste water treatment by means of urine separation

by Michael Friedrich Möderl

has been developed at and under supervision of the Unit of Environmental Engineering -

Institute of Infrastructure (formally Institute of Environmental Engineering) at the Leopold-

Franzens-University of Innsbruck.

The work was developed in the frame of the EU-project CD4WC (Cost-effective development

of urban wastewater systems for Water Framework Directive compliance). Thus, the content

of the thesis is therefore originating partly from joint efforts of members of the CD4WC-IUT

team together with M. F. Möderl. The team work resulted in the joint publication

Achleitner, S., Möderl, M. and Rauch, W. (2006).

CITY DRAIN © - A simulation software for integrated modelling of urban drainage

systems. Environmental Modelling and Software. (submitted).

Achleitner, S., Möderl, M. and Rauch, W. (2006).

Control of ammonium flux in urban drainage systems by urine separation – Evaluation

of options in an integrated context. Water Research. (in preparation).

Further use of results of this thesis for research and publications is to be approved by the

authors.


viii

EXECUTIVE SUMMARY

This diploma thesis explores a sustainable waste water treatment methodology by means of

urine separation. The aims are to increase efficiency of the waste water treatment plant and to

reduce the emissions into rivers. Main attention is directed to ammonium, because in urban

drainage systems this pollutant is primarily caused by urine. Base scenario is that the urine is

temporarily stored in urine tanks which are installed at special toilets (NoMix toilets). The

urine tank of the separation toilets discharges the temporarily stored urine into the sewer

system. The challenge is how the urine tanks discharge the urine to determine appropriate

strategies in order to reach the defined aims. Different control strategies for emptying the

urine tanks are developed. The aims “improved waste water treatment” and “decreased river

pollution” depend on each other. Thus, the control strategies are split in basic and interceptive

control options. The control strategies are tested on a modelled urban drainage system, which

has been calibrated.

With respect to the tested control strategies a reduction of ammonium emissions of 42% are

possible.


ix

ZUSAMMENFASSUNG

Die Gesellschaftspolitik bestimmt immer höhere Qualitätsanforderungen für die Rückführung

der Ressourcen in die Umwelt. Deshalb versuchte man in den letzten 10 Jahren im Bereich

der Abwasserbehandlung alternative Systeme zu entwickeln, die nachhaltiger sind als die

konventionellen Techniken. Erste Versuche der Abwasserbehandlung mit dem System der

Urinseparation in Schweden zeigen, dass mit dieser neuen unkonventionellen Methode einige

Vorteile in der Abwasserreinigung genutzt werden können.

Urinseparation ist auf Grund folgender Feststellung sinnvoll. Urin repräsentiert zwar nur 1%

der Abwassermenge eines Haushaltes aber beinhaltet 80% der Stickstoffmenge und 55% der

Phosphormenge im häuslichen Abwasser. Durch Abtrennung des Urins können Ammonium

und Phosphor spezifisch behandelt werden. (Experiences from the implementation of a urine

separation system: Goals, planning, reality; Department of Water Resources Engineering,

Lund University; 2004). Die Abtrennung erfolgt mit Separationstoiletten, an denen Behälter

für die Speicherung des Urins installiert sind. Diese Behälter fungieren als Zwischenspeicher.

Thema der Diplomarbeit ist wie diese Behälter entleert werden sollen. Dafür wurden

Steuerungsmöglichkeiten entwickelt und getestet.

Ziel dabei ist einerseits die Maxima und Minima der Ammonium-Fracht-Tagesgangline des

Kläranlagenzulaufs zu stutzen, damit die Reinigungsleistung der Kläranlage mittels einer

gleichmäßigeren (durchschnittlichern) Zulauffracht erhöht wird.

Andererseits sollen die Schadstoff-Emissionen in den Oberflächengewässern minimiert

werden. Durch die Entlastung der Mischkanalisation bei starken Regenereignissen entstehen

Überlaufe, die in Vorfluter geleitet werden. Tritt ein solches Regenereignis ein, kann man die

Entleerung der Urinspeicherbehälter unterdrücken und die Schadstoffe im Urin (Ammonium,

Hormone,…) können zurückgehalten werden.

Die Rückhaltung des Urins bei starken Regenereignissen hat zur Folge, dass die

Oberflächengewässer weniger mit Ammoniak, der entsprechend dem Ammonium –

Ammoniak Gleichgewicht gebildet wird, verschmutzt werden. Die Gewässer werden weniger

durch Hormonstoffe belastet, was zur Folge hat, dass in den Flüssen bessere

Lebensbedingungen, vor allem für Fische, entstehen.


1

1. INTRODUCTION

The requirements concerning the environmental compatibility increase permanently. Thus the

science looks for new methods to improve the sustainability of waste management processes.

This diploma thesis aims to increase the efficiency of the waste water treatment in order to

decrease the pollution in rivers.

Sources of urban waste water are among others washing machines, showers or toilets. The

amount of urine in the waste water is quantitatively small but the ammonium load in the urine

amounts to 80% of the total nitrogen in the waste water. Also the amount of phosphor load in

the urine contributes significantly to the phosphor load in the waste water. (Rauch et al.,

2002)

1.1. “CLASSICAL” URINE SEPARATION

One development of the last decade was the source separation of urine and faeces, aiming

finally at a reuse of nutrients. Specially designed toilets allow to separate, store and reuse

urine. Thus, applying the “classical” method of urine separation, urine is completely removed

from its original path from households to the wastewater treatment plant (WWTP).

From a legal point of view, collection and especially the reuse of urine is treated differently in

European countries. Where the utilization of urine as a fertilizer is allowed (e. g. Sweden)

(Kvarnström and Richert Stintzing, 2005), legal constrains are implemented in other countries

such as Switzerland or Austria. Although demonstration projects for source separation are

installed (Otterpohl at al., 2002), the reuse of urine is currently not permitted. Reasons for this

are manifold. The fear of groundwater contamination is the most important issue. Another

reason is that urban drainage systems and WWTP were installed over the last decades

associated with large investments. The implementation of urine separation and reuse

installations, under circumstances described above, would render parts of that investment as

useless, which is difficult from legal and consequently political reasons.


2

1.2. SANITARY FACILITIES FOR THE URINE SEPARATION

In order to separate urine from the remaining waste water, specially adapted toilets have to be

installed in the households. Such toilets are already in use and obtainable. (see Figure 1).

Figure 1: Urine separation toilet adapted for temporally urine storage (Rauch et al., 2002)

The principle is that urine and faeces are separated and discharged via different outlets. The

faeces drain will be automatically closed, if weight is released from the toilet seat. Thus, the

urine outlet is not influenced by water flushing.

Still a certain adaptation of people behaviour is required when using such toilet. As earlier

said, within Europe quite a number of implantation cases are known (e. g. Otterpohl at al.,

2002; Hancus et al., 1997) providing ample practical experience with regard to the daily use.

Thus, public acceptance with regard to use such a type of toilet seems to be obtainable also on

a larger scale (Berndtsson, 2005). Area wide implementation could possibly require decades.

The cost for such “NoMix” toilets without installation is approximately 700 EUR each

(Preisliste, Berger Biotechnik GmbH, 2005). Decreasing costs can be expected in case of a

future increasing demand.


3

1.3. ALTERNATIVE UTILISATION OF URINE SEPARATION

Since implementation of a full reuse of urine arises to be difficult, an alternative utilisation of

separation toilets is intended in this work. Goal here is not to eliminate urine from the urban

drainage system, but instead to influence its flow dynamics in the system. The focus lies on

ammonium, as the main substance associated with urine. First descriptions of this principle

are found in Larsen and Gujer (Larsen and Gujer, 1996).

The urine volume is small (1 % of the total waste water volume), but it is the source for a

significant amount (~80%) of the ammonium load in domestic wastewater. The ammonium

load pollutograph occurring in conventional systems depends on the human behaviour in a

catchment. Where less urine is generated during night hours, ammonium peaks are observed

during the day. This daily variation of ammonium delivered to the waste water treatment plant

(WWTP) has to be decreased.

Idea is to utilize the tank of the separation toilet as a buffer for the urine and to apply a

controlled emptying strategy to the single tanks. The control may be operated by centrally or

locally located devices. For both cases a coordinated discharge of urine flushes is wanted.

Where a control signal may be sent from the central station, information flow in the opposite

direction is not intended in this work. Primarily two improvements are wanted compared to a

conventional drainage system:

(1) First aim is to influence the daily variation of flow delivered from a drainage system

to the WWTP. Goal is to obtain an averaging ammonium load pollutograph of the

treatment plants inflow.

(2) During rain events, combined sewer overflow emissions have an negatively effect on

the rivers quality. Thus, second aim is to reduce the ammonium overflow loads into

rivers during rain events. If the urine tanks of the separation toilet buffer the

ammonium load during the rain, the ammonium overflow load will be reduced.


4

1.4. EXTENSION OF PREVIOUS WORK

Basic idea for such an approach presented by a research group at EAWAG (Swiss Federal

Institute of Aquatic Science and Technology) e. g. Larsen and Gujer (Larsen and Gujer, 1996)

and Rauch et al. (Rauch et al., 2002) presented the first detailed account of the principle of

urine separation in connection with a source control. This diploma thesis aims to continue this

work. The discussed measure is to be tested by use of numerical models. Following Rauch et

al. (Rauch et al., 2002) the production of urine is described by means of stochastic modelling

since a traditional catchment based modelling approach can not properly reproduce the

influence of urine separation at a single toilette level.

Going beyond the earlier approach by Rauch et al. (Rauch et al., 2002) this project includes

the following:

(a) Total of 11 control schemes, ranging from most simple approaches to complex

options, are tested

(b) Two scenarios are used for testing (simple virtual single catchment and semi

virtual case study from Tyrol, Austria – the system is real, but the urine

installation not)

(c) Evaluation of options using an integrated modelling approach including an

urban catchment and river system

(d) Sketching of implementation scenarios for the case study scenario

(e) Estimation of costs for different scenarios for economical implementation

Chapter 2 Materials and methods

5

2. MATERIALS AND METHODS

2.1. SOFTWARE WHICH IS USED FOR NUMERICAL MODELLING

Modelling of the integrated catchment as well as the testing of the measures has been realized

within the software CITY DRAIN. The software is developed at the Unit of Environmental

Engineering - University of Innsbruck - within a Matlab/Simulink environment. It allows the

integrated modelling of an urban drainage system including river systems (Achleitner et al.

2006).

In this chapter, the main features of the software CITY DRAIN are described. Further

examples for the main blocks which are used and the numerical models underneath are

provided. Detailed description of the software may be found either in Achleitner et al. 2006 or

in the software user manual.

Data collection as shown later in this work is based on the type of inputs required by the

software.

2.1.1. CITY DRAIN modelling environment

CITY DRAIN is an open source software. The software consists of a block library containing

elements that represent different compartments of the urban drainage system (Achleitner et al.

2006). The modelling environment applies with conceptual models for hydraulic and pollutant

transport of tracer substances.

Figure 2 shows a screenshot of the CITY DRAIN library. The library contains elements

which allow incorporation of input data. Required input data in urban drainage modelling can

be rain data or river flow at upstream locations.

Continuous modelling, using fixed discrete time steps, allows simulating for long term

periods. All compartments are using the same discrete time steps. These are usually

predefined by the temporal discretisation of the input (e.g. rain series) or time steps required

by the mathematical models which are applied.

Due to the software being developed in an open source environment it was possible to

- modify existing blocks

- implement the desired control strategies in form of new blocks.


6

These blocks will be part of CITY DRAIN 2, and are used within this project for the first

time. The software improvements which are made can be found as well in Achleitner et al.,

2006.

Figure 2: Screenshot of CITY DRAIN library (Achleitner et al., 2006)

2.1.2. Example blocks from the CITY DRAIN software

Modelling the transport path

Transport paths are modelled in the river blocks, the sewer block and the catchment blocks.

The hydraulics are described with a simplified muskingum model as shown in Achleitner at

al. (Achleitner et al., 2006). In the software CITY DRAIN transport paths are represented as a

series of reservoirs where the outflow of the previous reservoir is the inflow of the following

reservoir. In contrast to linear cascading reservoirs, the function of the storage volume of the

reservoirs in this model is

( 1 )

Therein the storage volume depends on the reservoir inflow (Qin) and the outflow (Qout). The

constant parameters K [s] and X [-] are the unit wave travelling time and the storage

coefficient, respectively. Derivation of the discrete form results in the following equation.

( 2 )

))()(()( tQtQXKtQKV outinout −⋅⋅+⋅=

)1(2/)2/( 1,

, XKtVXKtQ

Q iiiniout −+Δ

+⋅−Δ= −


7

Flows represent mean values for the last considered time step Δt. The parameter K [s] can

roughly be taken as the flow time applying for one reservoir. For numerical stability the

simplified muskingum model has to meet the following requirement:

( 3 )

In case this relation is violated, dynamic simulations may result in producing negative values

for flows. (Qout<0). It can be clearly seen that it is important to adjust modelling parameters K

and X to the chosen time step Δt. On the other hand one has to be careful in changing the time

steps Δt (e.g. enlarging Δt to gain shorter simulation time). The peak damping factor X is to

be chosen between the numerical extremes 0.0 (linear reservoir storage) and 0.5 (translation).

Catchment blocks

In CITY DRAIN 1.0 the flow routing in the catchment blocks was realized by a translation

model allowing multiple compartments. The transport model of the block has been changed to

a muskingum scheme which is used as well for flow and pollutant routing. Another novel

modification which was made is the introduction of locally generated dry weather flow

(DWF). Flows introduced from upstream are diverted to the upper most compartment. In

contrast, the DWF generated in the catchment itself is distributed evenly to all the

compartments. Thus, the underlying assumption is a geographical homogeneous distribution

of the generation.

Rainfall is processed by a loss model, prior being treated with the same routing scheme as

local DWF. The model is available for separate and combined sewer systems.

Combined sewer overflow structure (CSO)

The CSO block is based on a mass balance of inflow (QI), outflow (QE), overflow (QW) and

storage volume (V). Inflow (QI) in excess to the outflow (QE) causes - depending on the

current volume which is stored – either an increase in storage volume (V) or a respective

overflow (QW).

XtK

⋅≤

Δ≤

211


8

Pumping station (PS)

The pumping station is also not available within CITY DRAIN 1.0, but could be utilized for

this work (preversion of CITY DRAIN 2.0). The block follows a similar scheme to the CSO

and is based on mass balance. In extent the outflow, is regulated via set points for turning

pumps (respective outflows) ON or OFF. Further an emergency overflow (QW) is

implemented.

2.2. STOCHASTIC MODELLING OF THE URINE PRODUCTION

The generation of the urine production is modelled according to previous work made by

Rauch et al. (Rauch et al., 2002). Therein urine production in a catchment is treated on a

microscopic level, having stochastic features. Pollutographs, as used on a catchment scale,

can not be used for a proper description of single toilets. For a single toilet the urine yield is

rather described in the form of short pulses that are randomly distributed over the time. Since

the behaviour of each of the toilets in the catchment differs, the random distribution differs

not only over time, but as well for each toilet.

To model the urine and its diversion within the urban drainage system, the location of each

toilet element within a catchment would be required to be taken into account. As earlier

described, the modelling approach here is based on conceptual, catchment wise models. Thus,

within one catchment model, it has not been accounted for the specific location of single

households and flow paths in between. It is rather that the spatial distribution of households

within a sub catchment is considered to be evenly distributed. On a larger scale the

geographical distribution is represented by using different sub catchments.

2.2.1. Mathematical description of urine production

The aim is to generate the probability of a toilet use within a time step and the corresponding

amount of urine for each use. The mathematical description is adopted from Rauch et al.

(Rauch et al., 2002) who applied probability density functions (PDF) based on gamma

distributions.

)(

)/)(exp()(),,,(1

αβββα

αα

ΓΔ−−Δ−

=Δ−− xxxpdf ( 4 )


9

These are used to describe the theoretical probability of the use of a toilet within a time step.

Γ() and Δ are the gamma function and a positive offset along the x-axis, respectively. The

following PDFs with different parameters are used to describe the behaviour in the toilet

usage:

o The daily urine volume per person (UP); calculated as PDF(x, 5.315, 0.25, 0.5).

o The urine volume which is generated, each time a toilet is used (mu) by different

persons (theoretically independent from (UP)); calculated as PDF(x, 2.2, 0.06, 0.19).

o The number of persons that use the same toilet within a day (PWC); calculated as

PDF(x, 2.35, 0.29, 1).

Figure 3 shows the PDFs of UP, mu and PWC as defined according to Rauch et al. (Rauch et

al., 2002).

UP [Litre/pers/day]mu [Litre]PWC [Pers]

0 0.5 1 1.5 2 2.5 3 3.50

1

2

3

4

5

6Used gamma probability density function

Scale of variate

Pro

babi

lity

dist

ribut

ion

Figure 3: Used gamma distributions

Usually, not the number of toilets, but the population equivalent of a catchment is known.

Thus, the number of toilets in a catchment (nTOIL) has to be calculated by the population


10

equivalent (PE) divided through the expectation value (e) of persons per toilet and day

(PWC).

)(PWC

PEnTOIL ε= ( 5 )

The expectation value ε(PWC) is thereby calculated as

PWCPWCPWCPWC Δ+⋅= βαε )( ( 6 )

The aim is to compose a stochastically generated daily urine pollutograph for the total sub

catchment. Therefore the theoretical number of toilet uses per day (SUSE) has to be

calculated separately for each toilet. (SUSE) is equivalent to the urine volume generated by

persons per day (UP) multiplied with the number of persons per toilet and day (PWC) and

divided by the urine volume generated per use of a toilet (mu).

19.0)06.0,2.2(

)5.0)25.0,315.5()(1)29.0,35.2((+

++=

gamrndgamrndgamrndsuse ( 7 )

where gamrnd(a,b) is a gamma random number with the parameters a and b applied for the

different PDFs (UP, PWC and mu). Performing the same calculation not with random

generated numbers, but with the respective expectation values (e(UP), e(PWC), e(mu))

)(

)()(mu

PWCUPsuseMEAN εεε ⋅

= ( 8 )

leads to 9.55 toilet uses per day and toilet (SUSEMEAN).


11

To evaluate the probability of a toilet use within a time step, the probability distribution of a

toilet use within a day has to be ascertained. Basic assumption is that the ammonium load

pollutograph (of a sub catchment) complies with the probability distribution of the urine

volume within one day. Thus, the probability distribution function of the urine volume within

a day is represented by the ammonium pollutograph, normalized with a sum over the curve of

1 (see Figure 4).

0 6 12 18 240

1

2

3

4

x 10-3 Normalized ammonium pollutograph

time [h]

NH

4 [g

/m3]

Figure 4: Normalized ammonium pollutograph (time step size 300 s)

Now the probability of a toilet use within a time step (USE) is evaluated by multiplying the

probability of a toilet use within a day with the quantity of a toilet use per day (SUSE). For

example, the average value of a toilet use at 1200 is SUSEMEAN = 9.55 multiplied with 3.9*10-

3 (see Figure 4) which is scaled to the discrete time step Δt (here e.g. 300 [s]).

For using this method with random numbers, the procedure is:

Within each time step, a uniform distributed random number (RU) which ranges from 0 to 1 is

generated. This number is compared to the probability of a toilet use for this time step (USE).

In case RU is smaller than USE

)( mugamrndVUSER URINEU =⇒≤ ( 9 )

the respective urine amount generated for this time step (and toilet) is calculated using the

gamma random number based on the PDF(mu).


12

2.2.2. Implementation within an urban drainage model

The implementation within the urban drainage model was realized by creating a separate

“urine GenCon block” (Generation Control). Within this block, not only the urine production

and storage of the toilet systems is implemented, but also the numerics for all tested urine

control schemes are included. Figure 5 illustrates the different subroutines (production,

balancing and control) that are executed.

VT,i=0Vout,i=V'T,i

VT,i=Vmax

Vout,i=V'T,i-Vmax

Vout,i

VT,i=VT,i

Vout,i=Vout,i

V'T,i<VT,max

VT,i=V'Vout,i=0

V'T,i=VT,i-1+Vin,i

Emptying

V'T,i>VT,max

No Yes

VT,i-1

Vin,i Urine production

Emptying control Control-function

Loop

Volume balancing

Generation-function

Figure 5: Subroutines of the GenCon block

1) Urine production:

The urine production is implemented as stochastic event as described earlier (in 2.2.1).

2) Volume balancing:

In the model the single tanks are considered with a storage volume of four litres each. The

urine volume that is currently stored is balanced with the produced urine for each time step.

Thereby the used tank model can be described as followed:

o Tank overflow: If the urine tank is full, the produced urine will be discharged via an

overflow towards the sewer system.

o Tank emptying: Emptying of the tank is exclusively done by controlled valve

operation. The control signal is generated according to the used control strategy.

Within this work, different control strategies are tested which are explained at a later

point.


13

3) Emptying control

For the used control options a limitation regarding the tank features is applied. Thereby it is

assumed, that a tank, respectively the control unit which is installed, may receive signals, but

does not send any signal. Thus, for none of the tested control strategies, information on the

tanks current states (e.g. filling degrees) was available. If the control unit sends an emptying

signal to the receiver that is installed at the toilet, the tank of the separation toilet should be

emptied. The occurrence of an emptying signal depends on the urine control options (UCO)

which are illustrated in chapter 2.3. Figure 6 shows the GenCon block as implemented and the

underlying structure. The subroutines urine production and volume balancing are executed in

the generation function and the emptying control is executed in the control function.

Urine as ammonium sources

Load [g NH4/s]representing 20% of ammonium

representing 80% of ammonium

Load [g NH4/s]

C [g NH4/m3]Load/Q

C [g NH4/m3/(PE/WC) urine]

Ratio of ammonium loadOther ammonium / ammonium in urine

Other ammonium sources based on urine hydrograph

1DWF [m³/s,g/m³]

Urine GenCon

dwfrQDRr+ (dt)

DWF

PE

0.25

UAC

1dwf [m³/s/PE]

GenCon block submask

GenCon block

4t(+ dt) [m³/s]

3QDR [m³/s]

2rain [m³/s]

Generation function

Control function Generation function

Figure 6: Mask of the GenCon block

The following dynamic input signals are required for the GenCon block:

o dwf [m³/s/PE]…dry weather flow hydrograph scaled to unity regarding the PE

(population equivalent)

o r [mm/Dt]… the rainfall data as used for input to the model ( assumed online

measurement)

o QDR [m³/s]… effluent flow from a specific combined sewer overflow (CSO)

o r (t+Dt) [mm/Dt]…forecasted rainfall used for the interception control.


14

Static required inputs are entered via the block mask (see Figure 7).

2240

0.004

1

[1 1]

0.1

[0 7200 14400 21600 ... 86400]

[1.0 0.5 0.8 0.6 ... 1.0]

[0 7200 14400 21600 ... 86400]

[1.0 1.5 1.2 1.4 ... 1.0]

Population equivalent

Ammonium (NH ) concentration in urine [g /m ]4 NH4 URINE3 /(Pers/WC)

Toilet tank volume [m ]3

Number of pollutant components (n_comp=1) carried

Code for control option desired to apply

Flow for control options (used at ICO 2 for QDR)

Time steps used for generation of ammonium pollutograph [s]

Ammonium pollutograph y[g/m³]

Probability density function (PDF) (for BCO 3, only)

Time steps used for generation of PDF [s]

(explained later)

Figure 7: Block mask of GenCon block for static input parameters

In case several blocks are used, the input can either be entered for each block by using

globally parameters or by direct musk input.

Figure 6 shows that there are two paths for the generation of the output dry weather flow

(DWF). The first path generates the ammonium load of the urine volume, which is

representing ~80% of the total ammonium load. Only this source of ammonium can be

affected by the control option discussed here. The second path generates the ammonium load

of other sources (primarily “black water”) and represents ~20% of the total ammonium load.

Quantities from both sources (urine and non-urine stream) are related to each other via their

fraction percentage 80% : 20% = 1.00 : 0.25.

It is as well assumed that the ammonium load of other sources also follows the ammonium

pollutograph, but the ammonium load of other sources is not subjected to any control action

(thus no control block is used prior the Generation function block).


15

After adding both ammonium load streams the respective concentration in the dry weather

flow (dwf) is required. Utilizing the hydraulic flow of dry weather qDWF [m³/s] the respective

concentration is calculated as:

[ ]³/44

4 mgqMC NH

DWF

NHDWFNH =− ( 10 )

Output from both generation paths is the sum of urine discharged for a time step, based on the

number of toilets in the system. Therefore, the output represents the total catchment, but is

scaled – in average - by the expectation value ε(PWC). The scaled output is caused by the

stochastical algorithm of the urine generation.

To compensate the scaled output the urine ammonium concentration (UAC

[gNH4/m³Urine/(PE/WC)]) has to be multiplied with the expectation value ε(PWC)=1.6815.

As shown later, the UAC was used to calibrate the system.

The output signal of the GenCon block is the dry weather flow including the ammonium

concentration (DWF) [m³/s,g/m³].

2.3. TESTED CONTROL STRATEGIES

As described earlier, the effectiveness of different control strategies are tested regarding

diverse aims. A schematic overview is presented in Figure 8. Following the different weather

conditions (dry and wet weather), the strategies comprise of basic control options (BCO) and

interceptive control options (ICO).

The basic control options (BCO) are the main control options which – primarily – aim to

satisfy the goal of a more continuous ammonium load pollutograph at the WWTP.

If this control options are used exclusively, the overflow loads at CSOs will not be reduced.

Thus an interceptive control option (ICO) is designed to handle the reduction of ammonium

loads (peaks) in CSO overflow and rivers. Goal is to reduce the urine release during or prior

rain events such, that less ammonium is in the sewer system. Consequently this shall lead to a

reduction in overflow loads.

The basic control options (BCO) in connection with the interceptive control options (ICO)

result in the urine control options (UCO).

(BCO) + (ICO) (UCO)


16

The used syntax in numbering the “UCOs” is the BCO-number followed by the ICO-number.

For example, the fixed time basic control option (BCO=1) in connection with the interceptive

control option 2 (ICO=2) is called UCO 1-2.

None Fixed time Random PDFRandom

WeatherConditions

Interceptive Criterions

DWF

No criterions

r=0r>0

r=0 QDr<QDr,max

r>0r=0 QDr=QDr,max

R>0 r(+ t)>0r=0 r(+ t)=0

WWF

INTE

RC

EP

TIV

EC

ON

TRO

L

No interception No interception No interception

No emptyingNo interception

No interception

No interception

No interception

No emptying

No emptying

prob=0.2prob=0prob=c*t

prob=0prob=1

prob=1 prob=1prob=0.5 prob=0.5

prob=0prob=0

BASIC CONTROLS

3210

0

1

2

3

1 - 0 2 - 0 3 - 0

3 - 11

- 1 2

- 1 - a 2 - 1 - b

1 - 2 2 - 2 3 - 2

1 - 3 2 - 3 3 - 3

Figure 8: Control strategy options

2.3.1. Basic control options (BCO)

The basic control options are explained in this chapter.

2.3.1.1. No urine separation (BCO 0)

As a base line scenario, no modifications onto the current system are applied.

2.3.1.2. Urine tank control with a fixed emptying time (BCO 1)

The tank of the toilet empties the urine volume at fixed times once a day. The emptying times

are uniformly distributed over the time in each catchment. For example, if 24 toilets are in a

catchment, each hour a different toilet will empty its tank. Still, discharged volumes may vary

since the urine generation and consequently the filling of the individual tanks is subjected to

stochastically features. As already outlined earlier in the text the urine tank volume amounts

to 4 litres.

2.3.1.3. Urine tank control with a random emptying (BCO 2)

Emptying of tanks is based on uniform distributed random events with a probability of one

discharge per day. The exact time of the emptying for each tank is random, thus the empting

time changes each day. Theoretically it is possible that a tank discharges more than once a


17

day or in quick succession as well. Thus, single tanks may be released when they are already

empty or may be not released for more than a day and thus generate an overflow.

2.3.1.4. Urine tank emptying with a probability distribution function (BCO 3)

The urine tank emptying is again randomly distributed, but it is based on a probability

distribution function (PDF). To follow the PDF over the time, a probability of ten discharges

per toilet and day is used. The density function itself is derived as reciprocally proportional to

the ammonium load pollutograph (see Figure 4) to counteract the usual daily variation.

Similar to BCO 2, the single urine tanks could discharge the urine volume in quick

succession.

2.3.2. Interceptive control options (ICO)

To approach the reduction of ammonium peaks in rivers, the basic control (BCO) is

interrupted by interceptive control options (ICO). The interception control criterions are:

1) interruption of BCO only during rain events (from start to end)

2) interruption at rain events and if a maximum effluent flow (QDR,max) at a specific

combined sewer overflow is reached. Thus, the interception of urine release strategies

is temporally extended at the end.

3) interruption at rain events and if a “rain forecast r(t+Dt)” announces an upcoming rain

event. The rain forecast which is used here is assumed to be perfect without any

uncertainties associated. Forecast horizon is 1.5 hours resulting in a 1.5 earlier

interruption of the BCO.

2.3.3. Urine control options (UCO)

Depending on the BCO the interceptive control is different although the same criterion

initiating the interruption is used. The different interceptive control options (ICO) applied

onto different BCOs are explained in the following.

2.3.3.1. Fixed time control in connection with interceptive control (UCO 1-i)

If the basic control is not interrupted (UCO 1-0), only the aim of a continuous ammonium

load pollutograph at the (WWTP) will be pursued. The advantage is that this strategy does not

need a signal from a central station. The disadvantage is that the CSO ammonium load into

the rivers is not significantly reduced.


18

The interceptive control options used in UCOs UCO 1-1, 1-2 and 1-3 result that urine is not

released, but buffered in the tanks. Thus a maximum of 4 litres urine per toilet and rain event

can be retained. In case of single tanks reaching the maximum fill, overflow is produced and

discharged towards the sewer system. For these UCOs a signal from a central station is

needed.

Fixed time1

1 - 0

1 - 1

1 - 2

1 - 3

No interception

No emptyingNo interception

No interception

No interception

No emptying

No emptying

0 No criterions

r=0r>01

2r=0 QDr<QDr,max

r>0r=0 QDr=QDr,max

r>0 v r(+ t)>03 r=0 r(+ t)=0

r

r(+ t)

QDr

time

time

time

Figure 9: Fixed time control in connection with interceptive control options

2.3.3.2. Random control in connection with interceptive control (UCO 2-i)

If the basic control is not interrupted (UCO 2-0), only the aim of a continuous ammonium

load at the (WWTP) will be pursued. This control option does not necessarily need a signal

from a central station.

If (UCO 2-1-a) the basic control is interrupted caused by criterion 1-a, the basic probability

for the emptying used in the BCO 2 will change abrupt to zero at the start of a rain event. For

UCO 2-1-b the probability is set to the interceptive probability 0.2/day. At the end of the rain

event, the probability is set to the uninterrupted state. Where for UCO 2-1-a the return to a

probability of 1 is too sudden, a more linear increase is applied in UCO 2-1-b. At the end of

the rain event the interceptive probability is set to 0.5/day and increasing steadily up to

1.0/day within the following 2 days. The approach applied in UCO 2-1-a is taken from Rauch


19

et al. (Rauch et al., 2002), where the underlying idea is to avoid an overload of the WWTP

with stored ammonium after rain events.

In control strategy UCO 2-2 the emptying of the tanks is not executed at rain events. If the

maximum effluent flow (QDR,max) of a combined sewer overflow (CSO) is reached in a dry

period, the interceptive probability of emptying is set to 0.5/day. After this period, the

emptying probability is set back to the initial probability of 1.

Interception criterion three (ICO 3) is not applied for the BCO 2.

Random2

2 - 0

2 - 1 - a

2 - 2

No interception

prob=0prob=1

prob=1prob=0.5prob=0

0 No criterions

r=0r>01

2r=0 QDr<QDr,max

r>0r=0 QDr=QDr,max

r

QDr

time

time

2 - 1 - bprob=0.2prob=c*tr=0

r>01

Figure 10: Random control in connection with interceptive control options with r (rain), QDR (effluent flow of CSO structure) and prob (interceptive probability)

2.3.3.3. Random PDF in connection with interceptive control (UCO 3-i)

If the basic control is not interrupted (UCO 3-0), only a continuous ammonium load at the

WWTP will be pursued. This control option does not need a signal from a central station.

If the basic control is interrupted, the interceptive probability of a tank emptying is set back to

zero at a rain event (UCO 3-1). Thereby a reduced ammonium emission into the river is

reached.

In control strategy UCO 3-2, at rain events, the emptying of the tanks is not executed. If the

maximum effluent flow (QDR,max) of a combined sewer overflow (CSO) is reached in a dry


20

period the interceptive probability of emptying is set to 50% of the basic probability (10

emptyings per day in connection with the probability distribution function).

Random PDF3

3 - 0

3 - 1

3 - 2

No interception

No interceptionprob=0

prob=1prob=0.5prob=0

0 No criterions

r=0r>01

2r=0 QDr<QDr,max

r>0r=0 QDr=QDr,max

r

QDr

time

time

Figure 11: Random PDF control in connection with interceptive control options with r (rain), QDR (effluent flow of CSO structure) and prob (the interceptive probability)

2.4. QUALITY CRITERIONS OF URINE TANK CONTROL OPTIONS

In general three aspects are considered for evaluating the quality of the improvement gained

by a control measure. Comparison for all control options is made with regard to the reference

scenario that is the uncontrolled case 0-0.

Criterion 1 (CR 1) – Averaging indicator

First goal is to gain an averaging load at the WWTP inflow tending towards a

homogeneous flow to the plant in terms of ammonium load.

Criterion 2 (CR 2) – CSO emission indicator

Secondly, the measures are evaluated regarding their overflow quality – respectively

the improvement in overflow quality gained.

Criterion 3 (CR 3) – River immission indicator

Last - but not the less important - quality changes in the rivers themselves are

evaluated with respect to concentrations in the river.

A more detailed description of indicators is provided in the following.


21

2.4.1. Averaging of the ammonium load at WWTP (CR 1)

The physical parameter which is used here to evaluate the averaging of pollutants flowing into

the WWTP is the ammonium load mA [gNH4-N/s].

Under ideal conditions, a constant ammonium load would arrive at the WWTP throughout the

day, being equivalent to the mean loading derived from the daily variation. Thus, for the ideal

case no deviation between single ammonium loads and their average load would be observed

within a sampling period. To quantify the averaging of the pollutograph the absolute

deviations from their average may serve as an indicator. In order to quantify relative

improvement of a control option, the averaging is written dimensionless relative to the

averaging of the reference case.

∑

∑

−

−= N

iAiA

N

i

jA

jiA

mm

mmICriterion

00,

,

( 11 )

The reference scenario which is used is the uncontrolled case without any urine separation

applying. In formula 11 i is the actual time step [0 – N]. Index j denotes the UCO.

The criterion is not only applied under dry weather flow conditions, but as well for longer

periods including rain events.

2.4.2. Ammonium overflow load reduction at CSO (CR2 – Emission based)

The second aim is to reduce the discharge of ammonium from CSO. Thus, this criterion

applies at the emission side.

Considering a single overflow event at a specific CSO structure, different descriptive

parameters may be used. Herein overflow events are separated according to the rainfall

pattern. A dry period lasting longer than three hours is considered to separate rain events.

Within this dry period, the system is assumed to be empty again and to carrying dry weather

flow (DWF) exclusively.


22

Different parameters are used to characterise the single overflow events. All parameters are

based on mass flow m(t) [g/s] = Q(t) [m3/s] * C(t) [g/m3]. The following parameters are used

to characterize single events:

]/[,, sgm LKMAXGM …Maximum gliding mean of mass flow for a gliding interval

of ΔT=1hour

]/[, sgm LKMAX …Maximum peak of mass flow within the event

∫ ⋅=t

LKLK dtmeventgM ,, ]/[ …Mass flow within the event

In all three parameters the indices K and L denote the examined event and CSO structure

respectively.

In order to be able to compare different scenarios the number of descriptors has to be reduced.

The high number of overflow events, locations and different parameters which are available

prohibit a clear and straight forward judgment without aggregating the set of numbers. Goal is

to identify a single indicator as used earlier with criterion 1 (Averaging at the WWTP).

Derivation of a single parameter is shown exemplarily for the maximum gliding mean values LK

MAXGMm ,, . For each of the scenarios j, a mean and maximum value was calculated regardless

the spatial and temporal distribution of overflow events.

[ ]LKMAXGM

jMAXGM mmeanm ,

,, = ( 12 )

[ ]LKMAXGM

jMAXMAXGM mm ,

,,

, max= ( 13 )


23

Both are illustrated in Figure 12. It can be seen, that both follow the same pattern with regard

to different UCO.

0-0 1-0 1-1 1-2 1-3 2-0 2-1a 2-1b 2-2 3-0 3-1 3-2

UCO a-b

m

a-b

[g/s

]G

M,M

AX

mean of UCO

m - Maximum gliding mean GM,MAX

m GM,MAX

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Figure 12: Event based maximum gliding means for ammonium mass flow (means and maximums of UCO)

The maximum of the gliding mean is not needed as information for deriving a single

indicator, because the pattern of the mean value is similar. Mean values are normalized with

regard to the reference scenario.

0,

,,,

MAXGM

jMAXGMNj

MAXGMmmm = ( 14 )

To verify the normalized mean values as indicator, an alternative indicator has been used to

characterise the improvement. Thereby all CSO events of a specific urine control option (j)

are plotted against the corresponding data of the reference scenario (0-0). (see Figure 13).


24

In Figure 13 the event based maximums of ammonium overflow load are plotted against the

event based maximums of ammonium overflow loads of the reference scenario for each UCO.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

m

a-b

[g/s

]G

M,M

AX

m 0-0 [g/s]GM,MAX

0

m - Maximum gliding mean GM,MAX

(1-2)(1-1)(1-3)(2-2)(2-1)(2-1b)

(0-0) - Reference sc

enario

m GM,MAX

K

Gradient of lin. regression

Maximum of eventRegression analysis

Linear regression for UCO vs. 0-0 scenarioK

Incr

easi

ngim

prov

emen

t

1-K Indicator of improvement

Incr

easi

ngim

prov

emen

t

Figure 13: Linear regression analysis of event based results plotted against the baseline scenario

The regression analysis of the plotted points results in a regression line. The gradient of the

regression line represents the improvement of each UCO. Low gradients indicate a fine

improvement. Regarding the reference scenario (actual state) the relative improvement can be

written as 1-K for each UCO.

This procedure being present for event based gliding means is done as well for event

maximums ]/[, sgm LKMAX and cumulated overflow masses ]/[, eventgM LK . These leads to

finally six parameters describing in the same fashion the improvement gained for each urine

control option.

Table 1: Single indicators used in criterion II

Gliding mean ][,

, −Nj

MAXGMm ( )0,,,

,,,, , LK

MAXGMjLK

MAXGMj

MAXGM mmfk =

Maximum ][,

−Nj

MAXm ( )0,,,, , LKMAX

jLKMAX

jMAX mmfk =

Cumulated overflow mass ][

,−

NjM ( )0,,,, , LKjLKj MMfk =


25

Cumulating to a single indicator is done by applying the arithmetic mean to the six parameters

which are shown.

( )jjMAX

jMAXGM

NjNjMAX

NjMAXGM kkkMmmIICriterion +++++= ,

,,,,

61 ( 15 )

2.4.3. In stream ammonium peak concentration (CR3 – Immission based)

Ammonium in water exists in two species (Unionized NH3-N and ionized NH4-N) where fish

toxicity is attributable to the unionized ammonia. However due to the two ammonium species

being in equilibrium in water, immission based assessment can be made using ammonium

(NH4-N).

With regard to toxic effects, in stream concentrations are more important than long term mass

balances. Thus, immission based comparisons between UCO based on in stream

concentrations. Since toxicity depends not only on concentration levels, but as well on

duration of exposure, such approaches are as well included in derivation of an indicator value.

The procedure to indicate the emission based situation is used in a modified form. In stream

concentrations (C) are used instead of mass flows (m). A dry period lasting longer than 24

hours is considered to separate rain events. Applying the procedure presented in 2.4.2 leads to

finally 4 parameters for each UCO denoting as:

Table 2: Single indicators used in criterion III

Gliding mean ][,

, −Nj

MAXGMC ( )0,,,

,,,, , LK

MAXGMjLK

MAXGMj

MAXGM CCfk =

Maximum ][,

−Nj

MAXC ( )0,,,, , LKMAX

jLKMAX

jMAX CCfk =

As an additional parameter the maximum concentration limit (max(c)) that is continuously

exceeded for minimum one hour in the evaluation period is used. This parameter accounts –

similar to the 1 hour gliding mean – for the exposure time, here taken to be one hour. Relating

this concentration level to the concentration level found in the reference scenario, leads to the

parameters in normalized form. Mathematically the parameter is defined as:

)()(:],[:))(max(: ,,0

,, ττχ

χχ

jjjNj

N

NjNj CtCttttCC ≤Δ+∈∀== ( 16 )


26

A schematic illustration can be found in Figure 14.

Ammonium pollutograph

Dt

C (

t) [g

/m3]

j

time t [s]

DtDt

Dt

Dt

DtC(t)j

max(C(t))j

C(t)j

C(t)j

C(t)j

Figure 14: Scheme to evaluate Cj,N

Again the different parameters are combined using the arithmetic mean of the totally 5 values

for each UCO.

( )NjjMAX

jMAXGM

NjMAX

NjMAXGM CkkCCIIICriterion ,

,,,

,51

++++= ( 17 )

2.4.4. Application of evaluation criterions

In the previous chapters, three different evaluation systems are presented, where each of them

leads to a single indicator.

2.4.4.1. Criterion I and II (Averaging and emission based criterion)

For criterion I temporal distributed data measured at the WWTP inflow has been cumulated to

a single indicator. By applying criterion II temporal and spatial distributed data has been

evaluated event based and cumulated again to a single indicator (see Formula ( 15 )).

2.4.4.2. Criterion III (Immission based criterion)

Criterion III is evaluated separate at different locations in the river system. The purpose of

immission based criterion is comparing it with criterion II (emission based criterion) to

substantiate the values of the criterions.

Chapter 3 Test scenario A - Virtual catchment

27

2.4.5. Optimization the basic control options (BCO)

The separation toilets are installed with urine tank, are available with a capacity of 4 to 6

litres. The UCOs are tested with different tank volumes (4, 5 and 6 litres). The optimum is

reached with a tank fill of 4 litres.

The PDF of BCO3 had to be optimized. For sake of simplicity, optimization was based on a

model having urine as exclusive ammonium source. The non ammonium source described

earlier was not implemented at this point. Figure 15 shows the final evaluation (criterion I) of

the optimized daily ammonium pollutograph.

0-0 1-0 2-0 3-00

0.2

0.4

0.6

0.8

1

1.2

Criterion I

Control option

Aver

agin

g cr

iterio

n

0 6 12 18 240

10

20

30

40Ammonium load pollutograph

t [h]

Load

[g/m

3]

NoneFixed timeRandomRandom PDF

Figure 15: Criterion I and ammonium pollutograph (WWTP inflow) of calibrated BCO

Chapter 3 Test scenario A - Virtual catchment

28

3. TEST SCENARIO A - VIRTUAL CATCHMENT

A single combined sewer catchment is defined for a preliminary study of the desired control

strategies.

As input for dry weather flow, a unit hydrograph (flow per PE) is used (same as used for the

main sewer Reutte in the next chapter). The rain gauge data of the station Reutte is used as

rain input.

The virtual catchment is defined having a population equivalent (PE) of 2,000 inhabitants, a

runoff area (A) of 100 ha and a runoff coefficient (f) of 0.15. The flow time (tf) in the

catchment is taken to be one hour (see CITY DRAIN model in Figure 16).

urine

Urine Parameters

Urine GenCon

dwf

r

QDR

r+ (dt)

DWF

Urine GenConRAIN READ

RAIN READ

Rainread

[rain]

[rain][rain]

dwf

wc

wc

CSO

Typ BQi

Qw

Qe

Vi

CSO

CITY DRAIN CITY DRAIN CITY DRAINCITY DRAINPARAMETERS PARAMETERS

PARAMETERSPARAMETERS

CD Parameters

rl

DWFl

Qpl

Qe

Qe

CATCHMENT CSS

Catchment CSS

Figure 16: Test scenario „virtual catchment“

Downstream the catchment a combined sewer overflow (CSO) is installed. The CSO basin

volume is 200 m³. Maximum effluent flow (QDR) is 0.04 m³/s. The ratio of A*(f)/VCSO is

approximately 15 m³/haRED in accordance to ÖWAV-RB19 (ÖWAV, 2006). Population

density which is applied is ~133 PE/haRED which is as well in feasible range.

Chapter 4 Test scenario B - Case study Vils-Reutte

29

4. TEST SCENARIO B - CASE STUDY VILS-REUTTE

Prior to the model implementation and testing, the collection and preparation of data for the

“Case Study Vils” is made. Based on a structured data, the case study can be modelled and

calibrated. The drainage area of the “AWV-Vils-Reutte und Umgebung” (AWV-Vils) is taken

as case study. Figure 17 gives an overview on the connected drainage area.

Figure 17: Map of the case study Vils-Reutte

The upstream end of the Vils catchment (northwest part) is ‘Pfronten’, a town in Germany. It

is located along the river Vils, which runs from ‘Pfronten’ down to the WWTP and later to

‘Weißenbach’. From the south, the river Lech flows from ‘Weißenbuch’ through ‘Reutte’

meeting with river Vils further downstream.

The region Reutte is situated 800 m to 900 m above sea level. The climate is alpine, so the

region is characterized by cold winters and summers with intense rainfalls. The population


30

equivalent (PE) of all the villages is about 40,000. The average flow time (tf) of the sewer

system amounts 3 hours. The runoff areas (A) are in sum roughly 1,000 ha with an average

runoff coefficient (f) of 0.12.

In the region, two main sewer systems are installed, delivering wastewater to the WWTP. In

the northwest the main sewer Vils connects the catchments (CA) of 2 villages. In this area 3

combined sewer overflows (CSO) and one inline storage volume are installed.

The main sewer Reutte in the south connects the catchments (CA) of 11 villages. There are 4

CSOs and 7 pumping stations (PS).

Figure 18: Schematic map of the catchment Vils-Reutte


31

To simulate the catchment Vils Reutte with CITY DRAIN catchment data has been prepared

considering the different compartments of the sewer system as required by the software. Table

3 gives an overview on the required system data for the modelling.

Table 3: Data requirements for modelling Sub catchments (CA)

PE Population equivalents [-]

tf Flow time in the catchment [s]

A Sub catchment area [ha]

ϕ Runoff coefficient [-]

hV,I Initial loss [mm]

hV,D Permanent loss [mm/Δt]

Transport sewers

tf Flow time between catchment [s]

Combined sewer overflows (CSO)

V Basin volume [m³]

QDR Maximum effluent flow [m³/s]

Pumping stations (PS)

V Basin volume [m³]

QP Mean pumping rate [m³/s]

V(h)P,ON Level of ON set point of pumps [m]

V(h)P,OFF Level of OFF set point of pumps [m]

Rain data (r)

The rain data is to be provided as continuous measurement

Flow data

DWF Dry weather flow hydrograph. [m³/s] and [g/m³]

River Flow times of the river sections [m³/s]

4.1. DATA COLLECTION

4.1.1. Sub catchment (CA)

4.1.1.1. Population equivalents (PE):

The population equivalent (PE) of a sub catchment (CA) has to be evaluated to get the amount

of dry weather flow (DWF) per day. The AWV-Vils uses a fee scheme based on the

population equivalent (PE) for distributing costs among the connected villages. Thus, cost

distribution led to the distributed population equivalents of villages in the area. To further


32

estimate PE of sub catchment (parts of villages), the PE of a village has been distributed

according to the sub catchments areas.

4.1.1.2. Flow times (tf):

The software CITY DRAIN requires the flow time within a catchment. This is considered as

the maximum flow path of the waste water in a sub catchment (CA). Unfortunately, the

available sewer data only includes data of main sewers, but no data on regional sewers in a

sub catchment (CA). Secondly the flow time for runoff concentration can only be estimated.

Last, the waste water that originates from upper catchments – and is routed through the

system - has as well to follow the one pathway described by the flow time.

As an estimate the flow time (tf) of a sub catchment (CA) is considered as the sum of the flow

time (tf,1) in the main sewer that crosses the sub catchment (CA) (see Figure 19 - route AB)

and the flow time (tf,2) of the effluent flow from the furthermost point rectangular to the main

sewer (see Figure 19 - route PC).

The flow velocity of the route AB (the main sewer) was calculated. This was possible due to

available sewer data. Application of the Prandtl-Colebrook equation was based on the

assumption that the cross section is filled for ¾ of its height.

The flow time (tf,2) of route PC was evaluated assuming an average flow velocity of 1 m/s.

Figure 19: Scheme of theoretical flow routes within a sub catchment


33

4.1.1.3. Runoff areas (A):

The “Tiroler Raumordnungs-Informationssystem” (TIRIS) (Tyrolean information system on

regional development) provided an ortho photo of the region. On this basis it was possible to

– visually - define and digitize the boundaries of densely populated areas and its sub

catchments. Catchment areas were obtained using a CAD software.

4.1.1.4. Runoff coefficients (ϕ):

Runoff coefficients (f) for each sub catchment were estimated as well on basis of the ortho

photo. Parts of the sub catchments having visually different runoff coefficients (roads,

grass,…) were combined to a single (area) weighted runoff coefficient (ϕ).

4.1.1.5. Initial loss in the rain runoff (hv,i):

The initial loss (hv,i) depends on the depression storage and the hydraulic conductivity of the

ground. The value of 2.5 mm is situated in the middle of the spectrum [0.6, 5] mm found in

Lautrich (Lautrich, 1980).

4.1.1.6. Permanent loss of rain water (hv,d):

The permanent loss (hv,d) depends on the evaporation. The value of 1.25 l/d is assumed.

In Table 4 the data of totally 32 sub catchments located in the catchment Vils Reutte is

summarized. Overall, there is a total population of 37993 PE distributed over a total (gross)

area of 971 ha which is densely populated. 61 % of the PE and 54 % of the area A is

connected to a combined sewer system (CSS).


34

Table 4: Data of sub-catchments in the drainage Area Vils-Reutte Village # Catchment Type PE A tf ϕ hv,i hv,d [ha] [min] [-] [mm] [l/d]

Pfronten Pfronten 13105 211.6 1-1 Pfronten 1 CSS 1618 26.1 30 0.40 2.5 1.25 1-1-1 Pfronten 1-1 SSS 2556 41.3 30 0.40 2.5 1.25 1-2 Pfronten 1-2 CSS 4961 80.1 28 0.40 2.5 1.25V

ILS

1-3 Pfronten 1-3 CSS 3970 64.1 22 0.40 2.5 1.25Vils 2 Vils CSS 1731 94.5 22 0.52 2.5 1.25Pinswang SSS 485 19.5 3 Unterpinswang 295 11.8 33 0.55 2.5 1.25 4 Oberpinswang 190 7.6 20 0.58 2.5 1.25Musau SSS 374 23.0 5 Musau 256 15.8 29 0.51 2.5 1.25 6 Brandstatt 90 5.5 12 0.55 2.5 1.25 7 Roßschläg 28 1.7 3 0.60 2.5 1.25Pflach SSS 1277 59.4 8 Pflach O 781 36.3 28 0.50 2.5 1.25 9 Pflach W 496 23.1 31 0.45 2.5 1.25Reutte CSS 8289 187.5 10 Reutte N 3195 72.3 24 0.55 2.5 1.25 11 Reutte SW 2959 66.9 29 0.55 2.5 1.25 12 Reutte SO 2135 48.3 30 0.50 2.5 1.25Breitenwang 2728 101.1 13 Breitenwang CSS 814 30.2 33 0.50 2.5 1.25 14 Mühl SSS 1619 60.0 40 0.45 2.5 1.25 15 Lähn P CSS 150 5.5 7 0.65 2.5 1.25 16 Lähn L CSS 146 5.4 4 0.65 2.5 1.25Lechaschau 2148 79.9 17-1 Nord SSS 967 36.0 21 0.55 2.5 1.25 17-2 Süd SSS 1181 44.0 23 0.55 2.5 1.25Wängle 1394 35.2 18 Wängle CSS 1355 34.2 19 0.50 2.5 1.25 19 Holz CSS 39 1.0 6 0.60 2.5 1.25Ehenbichl 1521 31.1 20 Ehenbichl SSS 1138 23.3 20 0.50 2.5 1.25 21 Rieden SSS 383 7.8 14 0.65 2.5 1.25Höfen 1756 66.3 22 Höfen NW SSS 481 18.2 9 0.45 2.5 1.25 23 Höfen NO SSS 715 27.0 28 0.45 2.5 1.25 24 Höfen S SSS 560 21.1 15 0.55 2.5 1.25

RE

UT

TE

Weissenbach 25 Weissenbach SSS 1588 52.8 55 0.60 2.5 1.25 Heiterwang 26 Heiterwang SSS 707 27.6 24 0.50 2.5 1.25 Bichelbach 27 Bichelbach SSS 1264 5.0 6 0.50 2.5 1.3


35

4.1.2. Transport sewers

Transport sewers are defined as main sewers between sub catchments. There is no additional

waste water inflow along its flow path.

Figure 20: Scheme of flow routes between sub catchments

The flow velocity of the route AB was calculated based on sewer pipe data was available.

Application of the equation of Prandtl-Colebrook was based on the assumption that pipes are

filled up to ¾ of the maximum fill. Different sewer sections with changing cross sections

were considered in the calculations.

Table 5: Lengths (l) and flow time (tf) of transport sewers between sub catchments

ID l [m] tf [min] ID l [m] tf [min] ID l [m] tf [min]0-1 1200 16 8 1579 12 18 572 3

0-2 1800 25 9 281 4 19 2590 42

0-3 1400 12 10 1835 21 20 961 15

1 3169 28 11 496 6 21 630 8

2 1863 24 12 579 14 22 529 4

3 1453 13 13 385 5 23 1421 10

4 573 12 14 1038 16 24 277 5

5 1064 9 15 325 2 25 5257 87

6 323 3 16 762 8 26 4685 78

7 684 8 17 399 6


36

4.1.3. Combined sewer overflow structures (CSO)

Basin volumes (V) and maximum effluent flows (QDR) of each CSO structure were provided

by the “Planungsbüro Prantl”, a consulting engineer office, the AWV-Vils, and the municipal

office of Pfronten. Additionally measured overflow events of the CSO 1 were provided.

Table 6: CSO data CSO-ID V QDR [m³] [l/s]

1 800 3152 280 3253 134 22

RE

UT

TE

4 270 605 800 906 300 357 74 6V

ILS

8 200 80

4.1.4. Pumping stations (PS)

Besides the storage (basin) volumes (V) of each pumping station, pump characteristics such

as fixed (mean) pumping rates QP and ON / OFF set-points were assessed.

In all pumping stations only one pump was used – although the software CITY DRAIN is

capable of simulating multiple pumps. Usually, the pump rate of pumps depends on the pump

head which is to overcome.

A constant pump rate QP was estimated by the ratio QP/HP. The pump head HP was taken as

the difference of start- and end level of the pressure line including the head loss resulting from

friction. The water level in the basin was neglected.

The OFF and ON set points (hoff, hon) of the pumping station are transferred to volumes (Von,

Voff).

Recorded operating hours of the pump of the PS 1 were provided by the AWV-Vils.

Table 7: Pumping station data PS-ID Pumping Station Sewer QP Voff Von V [m³/s] [m³] [m³] [m³]

1 Klosterweg 02 0.175 - - 18.50 2 Rieden 08 0.008 0.40 3.27 2.87 3 Musau 09 0.011 0.22 0.92 0.70 4 Roßschläg 09 0.009 0.23 0.91 0.68 5 Oberpinswang 10-1 0.011 0.40 2.71 2.31 6 Unterpinswang 10-2 0.011 0.40 2.85 2.45 R

EU

TT

E

7 Heiterwang 12 0.0110 - - 22.15


37

4.1.5. Rain data (r)

The rain data was provided as cumulated volumes [mm/m²] for 10 minute time intervals. The

available data was a continuous long term series from 1998-2004 (UTC time scale) provided

by National Meterological Institute ZAMAG („Zentralanstalt für Meteorologie und

Geodynamik“). The ammonium concentration of water in storm water pipes was taken to be

1 g NH4/m³ according to data records found in Brombach and Fuchs (Brombach and Fuchs,

2003).

4.1.6. Flow data

4.1.6.1. Dry weather flow

The aim was to calibrate the sewer system model using original dry weather flow data. Thus,

an inflow hydrograph at the WWTP for dry weather conditions was evaluated on basis of an

inflow data series from Jan. – Oct. 2004. Data was provided as 15 minute mean values

measured at the WWTP inflow. Different daily hydrographs were averaged to single

representative hydrographs. Thereby, only days without precipitation on the day before and

on the corresponding day were used. This was to exclude the influence of previous rainfalls

onto flow measurement.

An analysis of hydrographs in tourist season reveals that the DWF does not change

significantly compared to regular seasons. A different pattern will be seen, if workdays (Mon-

Fri) are compared with the weekend (Sat-Sun). Therefore a dry weather flow hydrograph was

composed for workdays and weekends, separately.

Flow measurements at the WWTP were made separate for the two main sewers Reutte and

Vils, resulting in a separate assessment of each.

For preparing the data, the time data had to be corrected for UTC (Coordinate Universal

Time). Reason was that the time scales which was used was the MEWT (Middle European

Winter Time) and the MEST (Middle European Summer Time), respectively. In order to have

a uniform – and continuous - time scale throughout the year, both MEWT and MEST were

corrected for UTC with +1 and +2 hours, respectively.


38

The mean DWF hydrograph is based on a sample of 35 days for workdays, and 15 days for

weekends.

0 6 12 18 240

0.02

0.04

0.06

0.08

0.1DWF REUTTE

time [h]

Q [m

3/s]

0 6 12 18 240

0.02

0.04

0.06

0.08

0.1DWF VILS

time [h]

Q [m

3/s]

Workdays DWFWeekends DWF

Workdays DWFWeekends DWF

Figure 21: DWF of main sewer Reutte and Vils at workdays and weekends

4.1.6.2. Flow data of the river Vils and Lech

The flow data was provided as flow rate [m³/s] for each of the 15 minute time steps. The

available data was a continuous long term series from 1998-2004 (UTC time scale) provided

by the National Hydrologic Institute („Hydrologischer Dienst Tirol“). Two measuring stations

are in the region, which are shown in Figure 22.

Figure 22: River gauge stations of Vils and Lech


39

For modelling purpose, gauge measurements from downstream were displaced and used as

upstream input. Comparison of downstream measured flows and modelled flows (incl.

displacement) are nearly identical.

At these stations the flow velocity is measured as well, but only once a month. The flow

velocities were averaged to estimate the flow time. Rain water from separate sewer systems

sub catchments (CA), combined sewer overflows (CSO), waste water of emergency spillway

of the pumping stations (PS) and the outflow of the waste water treatment plant (WWTP)

discharge polluted water into the river sections.

Table 8 and Figure 23 give an overview on the river stretches and the discharge points which

are defined.

Table 8: River data

Section l v t [m] [m/s] [min]

7-3-1 1500 0.79 307-3-2 5000 0.79 1057-3-3 1210 0.79 253-4-1 1232 0.79 25VI

LS

3-4-2 500 0.79 104-5 1238 0.60 35 6-1-4 2000 1.23 256-1-3 3956 1.23 556-1-2 2000 1.23 256-1-1 2000 1.23 251-2 3174 1.23 452-4-2 3100 0.30 1702-4-1 2805 0.30 155

LEC

H

2-5 4392 1.23 60

Figure 23: River scheme and inflow points


40

Based on a measurement report made by the IUT (Unit of Environmental Engineering) the

ammonium concentration of the river Vils was taken to 0.016 g NH4/m³ (see Figure 23 P7)

and the ammonium concentration of the river Lech was taken to 0.02 g NH4/m (see Figure 23

P6).

4.2. MODEL CALIBRATION

After the data collection, the case study Vils is modelled with CITY DRAIN (see Figure 24).

Using the model the collected parameters were included as a starting point for further

calibration. Hydraulics as well as ammonium were calibrated in the base line scenario.


41

Figure 24: Scenario B – City Drain model of catchment Vils Reutte


42

4.2.1. Indicators for calibration quality

To evaluate the quality of the calibration, quality indicators are defined to compare the

measured data (M) with the simulated data (S). The indicators shown in the following are

taken from M. Grecu, W.F. Krajewski (Grecu and Krajewski, 2000).

4.2.1.1. Correlation coefficient (C)

The correlation coefficient (C) compares the total variance of the two independent variables.

Thus, this value is not suitable to compare measured data (M) with simulated data (S) since

(C) represents the quality of a linear regression. Additionally the correlation coefficient (C) is

not sensitive enough to differentiate proportional between the measured data (M) and

simulated data (S). Thus the correlation coefficient is not applied.

4.2.1.2. The coefficient of efficiency (E)

The coefficient of efficiency (E) is defined as

∑

∑

=

=

−

−−= N

ii

N

iii

MM

SME

1

2

2

1

)(

)(1 ( 18 )

The interval of E ranges from minus infinity (-∞) to 1. A larger value indicates a better

agreement. If E is less than 0, the simulated data (S) matches worse than the mean value of

the measured data.

4.2.1.3. Index of agreement (d)

The index of agreement (d) ranges from 0 to 1. A large value indicates a good agreement.

2

1

2

1

)(

)(1

∑

∑

=

=

−+−

−−= N

iii

N

iii

MSMM

SMd ( 19 )


43

4.2.1.4. The multiplicative bias (B)

The bias (B) evaluates the ratio of the means.

MSB = ( 20 )

4.2.2. Calibrating the dry weather flow (DWF)

The catchment blocks in the simulation environment utilize the dry weather flow (DWF)

pattern as dynamic input, which had to be evaluated. The objective was to create a simulated

dry weather inflow to the WWTP which is – as far as possible – similar to the measured

pattern.

Measurements are limited to the WWTP inflows from the main sewers Vils and Reutte.

At the catchment level a unit waste water hydrograph (per PE) was used, scaled by the daily

mean DWF to calibrate magnitudes. For the unit DWF hydrograph a cubic spline with 6 data

points was used which has an area of 1 under the curve. Modifying the unit hydrograph

allowed to fit the daily variation without changing daily mean flow magnitude (pattern

calibration). In absence of better knowledge, the same pattern was used in all sub catchments.

Points were varied such that simulation produced the correct shape and magnitude of flow at

the WTTP. This was done for the main sewer systems Vils and Reutte, for workdays and for

weekends.

0 6 12 18 240

0.02

0.04

0.06

0.08

0.1

time [h]

Q [m

3/s]

0 6 12 18 240

0.04

0.06

0.08

0.1DWF REUTTE - Weekends

time [h]

Q [m

3/s]

DWF MeasuredDWF Simulated

0.02DWF MeasuredDWF Simulated

DWF REUTTE - WorkdaysE= 0.97d= 0.99B= 1.01

E= 0.97d= 0.99B= 1.01

Figure 25: Simulated and measured DWF hydrographs main sewer Reutte


44

The daily waste water volume in sub catchments according to the FAAT (ÖWWV, 1982)

amounts 200 l/PE (value for design). Having this mean daily DWF as starting point, the

calibrated mean of the daily DWF at the main sewer Reutte came out to be 213 l/PE. This

calibrated value is in the order of magnitude with the recommended value for design purpose

of the guideline.

E= 0.96d= 0.99B= 1.02


0.02

0 6 12 18 240

0.02

0.04

0.06

0.08

0.1DWF VILS - Workday

time [h]

Q [m

3/s]

0 6 12 18 240

0.02

0.04

0.06

0.08

0.1DWF VILS - Weekends

time [h]

Q [m

3/s]


E= 0.97d= 0.99B= 1.00

Figure 26: Simulated and measured DWF hydrographs main sewer Vils

The calibrated mean DWF in sub catchments at the main sewer Vils amounts 111 l/PE. The

difference to the guideline value is the result of a little water demand of the village Vils,

which amounts 63 l/PE/day. In contrast to the water demand of the village Vils, the water

demand of the village Reutte amounts 154 l/PE/day (design value 150 l/PE/day (ÖWWV,

1982)). The water demand values were taken from the payment scheme that has been

provided by the AWV-Vils.

4.2.3. Calibrating the wet weather flow (WWF)

Within CITY DRAIN it is as well possible to cover rainfall runoff processes. Precipitation is

thereby drained either via combined sewer pipes (towards the WWTP) or storm sewer pipes

(discharged to the river). To calibrate the wet weather flow (WWF), which is the

superposition of calibrated DWF and rainfall runoff, an observation period of 72 days of the

year 2004 (August – October) was used. The objective was to create a simulated inflow of the

WWTP which meets the measured flows with least possible error.


45

The model parameters which were modified to calibrate the system are:

o the runoff coefficient (f) and the initial loss (hv,i) of the catchment blocks to handle

the amount of the wet weather flow hydrograph,

o the maximum effluent flow (QDR) of the CSO to handle maximum flows delivered

downstream,

o and the number of sub reaches of the sewer blocks to handle the flow time (tf) of the

sewer system.

Optionally, the parameter X of the muskingum sewer software block can be modified, for a

fine tuning of the calibration. Finally this option was not used, because it would – in this case

- not result in a significant improvement of the calibration.

To quantify the calibration quality using the quality indicators shown earlier, the measured

data and simulated data were separated for different rain events. A rain event is thereby

defined as an interval, where rainfall (r) is not interrupted longer than 3 hours (mean flow

time of the main sewer Reutte). In Figure 27 and Figure 28, the simulated maximum flow rate

(QMAX) and waste water volume (V) of each rain event were compared with the measured

values.

The quality indicators coefficient of efficiency (E), index of agreement (d) and the

multiplicative bias (B) were calculated for the maximum flow rates (QMAX).

Main Sewer Reutte

0 0.05 0.1 0.15 0.2 0.250

0.05

0.1

0.15

0.2

0.25

QMAX Measured [m3/s]

QM

AX S

imul

ated

[m3/

s]

0 2e3 4e3 6e3 8e3 10e30

2e3

4e3

6e3

8e3

10e3

V Measured [m3/event]

V Sim

ulat

ed [m

3/ev

ent]

Main Sewer ReutteE= 0.74d= 0.93B= 0.94

E= 0.97d= 0.99B= 0.98

Figure 27: QMAX and volume ratio of main sewer Reutte


46

The quality indicators coefficient of efficiency (E), index of agreement (d) and the

multiplicative bias (B) were calculated for the volumes (V).

0 0.05 0.1 0.15 0.20

0.05

0.1

0.15

0.2

QMAX Measured [m3/s]

QM

AX S

imul

ated

[m3/

s]

0 1e3 2e3 3e3 4e3 5e30

1e3

2e3

3e3

4e3

5e3

V Measured [m3/event]V

Simul

ated

[m3/

even

t]

E= 0.51d= 0.85B= 0.85

E= 0.87d= 0.97B= 0.99

Main Sewer Vils Main Sewer Vils

Figure 28: QMAX and volume ratio of main sewer Vils

The correlation of the main sewer Vils is less than the correlation of the main sewer Reutte.

This is due to two reasons:

o no rain data was measured near the villages of the main sewer Vils. The rain data that

was measured in the village of Reutte was also used to compute the rainfall runoffs of

the sub catchments (CA) in the main sewer system Vils.

o no longitudinal section of main sewer Vils was available thus the flow velocity was

estimated with 1 m/s.


47

In Figure 29 and Figure 30, a sample of a rain event with high rain intensity is plotted.

1.54 1.56 1.58 1.6

x 106

0

0.05

0.1

0.15

0.2

0.25

cd time [sec]

Q [m

3/s]

WWF MeasuredWWF Simulated

Sample WWF Reutte

E= 0.70d= 0.94B= 0.88

Figure 29: WWF sample of Rain event main sewer Reutte

1.54 1.56 1.58 1.6

x 106

0

0.05

0.1

0.15

0.2

cd time [sec]

Q [m

3/s]

WWF MeasuredWWF Simulated

Sample WWF Vils

E= 0.74d= 0.93B= 0.86

Figure 30: WWF sample of Rain event main sewer Vils

Additively, the simulated number of CSOs of the most important CSO structure (CSO 1) was

compared with the logged overflow data. Difficulties occur due to the data not being logged

regularly. Dealing only with data of the number of overflows allows only a very rough

estimation on events occurring or not, but allows no distinct calibration for overflow volumes.

The pumping station with the highest wet weather flow has a data logger which is measuring

the operating hours of the pumps. This data obtained thereby was compared to the pumping

rates of the simulation.


48

4.2.4. Calibrating the ammonium pollutograph

First of all, the daily ammonium load at the WWTP inflow was calibrated. The ammonium

concentration pollutograph of the urine was composed with a normalized 12 point polygon

(see Figure 31 Normalized ammonium pollutograph). The mean concentration of ammonium

in urine (UAC) was modified to obtain the correct magnitude of load (mean ammonium

concentration of simulated and measured data agreed). Since the UAC is – numerically –

scaled for the expectation value ε(PWC), the concentration is related to the number of PE/WC

present in a catchment. Measured ammonium concentration resulted from field measurements

by IUT (Unit of Environmental Engineering). The ammonium concentration was measured at

the 28.7.2005 having 2 hour composite samples. The calibrated mean urine concentration

UCA was

[ ])//(³/4240,2 WCPEmgNHUCA = ( 21 )

As the UCA value was used for calibration parameter, the urine concentration was

crosschecked with literature values of ammonium concentration in urine. UCA is to be scaled

by the expectation value of PWC.

( ) [ ]³/4770,36815.1240,24 mgNHPWCUCAC UNH =⋅=⋅=− ε ( 22 )

The urine ammonium concentration found in the literature (Jönsson et al., 1997) was in the

range of 3,600-3,900 [g NH4/m³]. Thus, the UCA value which is calibrated is in agreement to

literature data.

0 6 12 18 240

1

2

3

4

DWF ammonium pollutograph

time [h]

UA

C [g

/m3]

norm

NH

4

NH4 MeasuredNH4 Simulated

x 10-3Normalized ammonium pollutograph

0 6 12 18 24time [h]

0

10

20

30

40

NH

4 [g

/m3]

E=0.59d=0.85B=1.01

Sub catchment / Toilet scale WWTP

Figure 31: Ammonium pollutograph of a sub catchment and the WWTP inflow

The finally used model parameters fixed after calibration are summarized in the attachment.

Chapter 5 Results – Evaluation of control strategies

49

5. RESULTS – EVALUATION OF CONTROL STRATEGIES

The different urine control options were tested on scenarios A and B. Test scenario A

represents a virtual catchment with a short flow time and a homogeneously population

density. Test scenario B represents a real world sewer catchment with a large flow time and

an inhomogeneous population density.

5.1. TEST SCENARIO A - VIRTUAL CATCHMENT

Using the test scenario A the evaluation of the basic control options (BCO) in connection with

the interceptive control options (ICO) are tested for dry weather (DWF) and wet weather

(WWF) conditions.

5.1.1. Basic control options (BCO) at DWF

For dry weather conditions, only the aim of averaging the ammonium pollutograph at the

WWTP is relevant. Figure 32 shows the BCO evaluated for criterion I, the averaging criterion

0-0 1-0 2-0 3-00

0.2

0.4

0.6

0.8

1

1.2

Criterion I

Control option0 6 12 18 24

0

10

20

30

40

Ammonium load pollutograph

t [h]

Load

[g/m

3]


Aver

agin

g cr

iterio

n

Figure 32: Criterion of averaging; ammonium pollutograph (DWF, virtual catchment)

The fixed time control option (BCO 1) is found to be the optimum under these conditions.

The peaks observed at the uncontrolled ammonium load pollutograph will be nearly evened

out, if this control strategy is applied. The different filling degrees of the tanks at emptying

time causes fluctuations in the ammonium load pollutograph.


50

The random PDF (BCO 3) is found to be the worst BCO. The emptying probability which is

used is a factor 10 higher than in BCO1, which results in a nearly “uncontrolled” behaviour.

On the other hand, lowering the emptying probability reduces the main feature of the PDF.

This is to establish increased emptying probabilities in times of low loads (under uncontrolled

conditions) and vice versa.

The random control option (BCO 2) does not achieve as good results as the BCO 1. Extremes

that are seen in the uncontrolled case are still present. This may results from the fact that

discharges are equally probable during all the day. Same release probability is applied as well

for times when tanks are filled predominantly (at times with high urine production).

5.1.2. Urine control option (UCO) at WWF

At wet weather conditions both the aim of averaging the ammonium load pollutograph at the

WWTP and the aim of a reduced overflow load into rivers are relevant.

5.1.2.1. Criterion I - Averaging of ammonium load pollutograph at WWTP

At wet weather flow conditions, best control strategy is – again - the fixed time control option

without any interruption (UCO 1-0). Options UCO 1-0, 2-0, 3-0 are better than their

respective UCOs which are additionally controlled using ICOs, due to the interruption of the

BCOs.

BCO 1 compared to all others is resulting in an improvement between 35-60%. BCO 2 type

options range in the middle of all control options. BCO 3 types are the worst with an

improvement of ~10%.

Aver

agin

g cr

iterio

n

0

0.2

0.4

0.6

0.8

1

1.2

Criterion I

Control option0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Figure 33: Criterion I for Scenario A (virtual catchment-WWF)


51

5.1.2.2. Criterion II - Reduced ammonium overflow load at CSO

The fixed time emptying control with an interception that depends on rainfall (UCO 1-2)

seems to be best with regard to criterion II. An improvement of 22% is gained compared to

the baseline scenario (0-0). Lowest improvement is again observed by BCO 3 types, which

are based on random emptying in connection with a PDF. Finally BCO 2 types range in

between.

0

0.2

0.4

0.6

0.8

1

1.2

Criterion II

Control option

Emiss

ion

crite

rion

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Figure 34: Criterion II for Scenario A (virtual catchment, WWF)

All in common is that options which are subjected to an additional ICO – as expected (and

intended) - have better performance regarding pollutant overflow loads. Largest improvement

is gained by option 1-2 with a relative improvement of ~25% compared with the baseline

scenario.


52

5.1.3. Choosing best control strategy (Combination of CR1 and CR2)

The uniformly weighted criterion combines both, the aim for a averaging load at the treatment

plant inflow and a reduction of ammonium overflow load from CSOs. As earlier outlined,

criterion I (averaging) and criterion II (emission criterion) are combined as mean value of

both, applying equal weight for both.

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2Control optionControl option

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-20

0.2

0.4

0.6

0.8

1

1.2

Criterion I+II

Figure 35: Criterion I+II (uniform weighted) for scenario A (virtual catchment, WWF)

Fixed time control options are reaching the best results. The results of random and random

PDF control options don’t comply with the expectations.

UCO 1-i are in the same range. UCO 1-2 needs a measuring equipment to gauge the

maximum effluent flow (QDR) at a CSO. UCO 1-0 is only levelling out the ammonium load

pollutograph at the WWTP inflow, no interceptive control is used. UCO 1-1 is to favour,

because UCO 1-1 causes low installation costs (it require only rain measurement) and reduces

overflow loads unlike UCO 1-0.

The implementation of a sewer system by means of urine separation will take a long time.

The time depends on the “life expectancy” of a toilet. Thus, the fixed time control with an

interruption that depends on rainfall (UCO 1-1) – the best of control – is evaluated at different

upgraded times to estimated how many years it will takes to reach a wanted result.


53

Figure 36 shows that both, the emission based criterion (criterion II) and the averaging

criterion (criterion I) are nearly proportional to the percentage of separation toilets.

SINGLE CATCHMENT

0 0.25 0.5 0.75 10

0.2

0.4

0.6

0.8

1

Percentage of separation toilets

Crit

erio

ns

Criterion ICriterion II

UCO 1-1 (fixed time control)Stepwise implementation

Figure 36: Efficiency of control strategy UCO1-1 vs. degree of implementation


54

5.2. TEST SCENARIO B - CASE STUDY VILS

The proposed basic control options (BCO) with associated interceptive control options (ICO)

are as well tested and evaluated using scenario B - catchment Vils Reutte. The testing is done

for a dry weather (DWF) and wet weather (WWF) conditions as described later in this

chapter.

5.2.1. Base control options (BCO) at DWF

At DWF conditions a more averaging load at the WWTP of ammonium is aimed.

Consequently criterion I is applied exclusively for evaluation.

The results are similar to the ones obtained using scenario A (virtual catchment).

Pollutographs which are generated are generally smoother than the ones obtained for the

virtual catchment. The amplitudes of the overtones on the ammonium load pollutograph are

decreased compared with the virtual catchment conditions. Reasons may be the larger

catchment (39,000 PE) which is used.

0-0 1-0 2-0 3-00

0.2

0.4

0.6

0.8

1

1.2

Criterion I

Control option

Aver

agin

g cr

iterio

n

0 6 12 18 240

200

400

600

800

Ammonium load pollutograph

t [h]

Load

[g/m

3]


Figure 37: Criterion of averaging; ammonium pollutograph (DWF, case study Vils)

Figure 37 shows results for criterion I (averaging) for each UCO. The averaging using the

fixed time control (UCO 1-0) is reduced down to ~20% of the baseline scenario. Second best

option is the UCO 2-0 decreasing to ~50% of the original. Only ~15% reduction is observed

for UCO 3-0 which produces a – visually – nearly equal daily variation.


55

5.2.2. Urine control option (UCO) at WWF

5.2.2.1. Criterion I at WWF

The results of criterion I (averaging) are similar to the results from scenario A - virtual

catchment. Distribution for preferential and non preferential options is alike, where reductions

obtained are somewhat larger.

At wet weather flow conditions, the best control strategy is the fixed time control option

without an interruption (UCO 1-0). The random control options (UCO 2.i) are nearly at the

same level as UCOs 1-1, 1-2 and 1-3.

0

0.2

0.4

0.6

0.8

1

1.2

Criterion I

Control option

Aver

agin

g cr

iterio

n

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Figure 38: Averaging of ammonium load pollutograph at WWTP (WWF)

5.2.2.2. Emission based criterion (criterion II)

Criterion II represents the reduction of ammonium overflow load at CSOs.

Again, results are similar to the results from scenario A - virtual catchment, having somewhat

larger reductions than in the virtual catchment.

Best result is obtained by UCO 1-1. The reductions gained by random control options (UCO

2-i) are in the same range of UCO 1-i or less than UCO 1-i.


56

The total ammonium overflow load of the baseline scenario accounts for ~3,700 kg-NH4. The

total ammonium overflow load using UCO 1-1 accounts for ~1,570 kg-NH4. About 42% less

ammonium is spilled than without control.

0

0.2

0.4

0.6

0.8

1

1.2

Criterion II

Control option

Emiss

ion

crite

rion

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Figure 39: Evaluation of UCO regarding criterion II

5.2.2.3. Immission based criterion (criterion III)

The immission based criterion (criterion III) indicates the influence of pollution in rivers.

Criterion III is – in contrast to criterion II – evaluating concentrations instead of event based

loads. Further, not combined evaluation of more locations is performed. Therefore,

criterion III was evaluated for four different points in the river system. Points considered are

shown in Figure 40:


57

o Point I12: Lech before low flow stretch

o Point I341: Vils, before WWTP

o Point I342: Vils, after WWTP

o Point I45: Lech, before hydropower station

Figure 40: Evaluation points of CR III

Comparison of criterion II (emission criterion) and criterion III (immission criterion)

demonstrates the similar tendency with regard to the different control strategies which are

tested. In Figure 41 criterions II and III are shown, next to the temporal variation of

concentrations in the baseline scenario. Points prior the WWTP show that an increase in

concentration is observed. For river Lech this increase is less due to the larger flow - and

therefore a larger dilution - in the river. Downstream of the WWTP larger concentrations are

observed. Fluctuations in the time series are attributable to the limited abilities of the WWTP.

The WWTP in the model is considered as “perfect” treatment plant. Concentrations are

reduced such, that emission criterions (outflow limit concentrations and degree of treatment)

are fulfilled. Depending on the inflow, this scheme – where data is fed through directly and

not buffered - causes fluctuations in the concentrations.

As seen, reductions which are visual in the river are not as large as observed in criterions I

and II. This is attributable to the dilution in river having background concentrations of 0.02

and 0.016 gNH4/m³. Still, evaluations based on criterion I and II meet the outcome to

evaluations by criterion III.

In Figure 41 the concentrations temporal increase at I 342 and I 45 caused by temporal

decrease of the river flows.


58

1.9 2 2.1 2.2 2.3 2.40

0.1

0.2

1.9 2 2.1 2.2 2.3 2.4x 107

0

0.1

0.2

C [g

/m3]

1.9 2 2.1 2.2 2.3 2.4x 107

0

0.1

0.2

0.3

1.9 2 2.1 2.2 2.3 2.4x 107

0

0.1

0.2

I 341 - VILS, before WWTP

I 342 - VILS, after WWTP

I 12 - LECH, before low flow stretch

I 45 - VILS/LECH, before hydropower station

C [g

/m3]

C [g

/m3]

0.3

0.3

C [g

/m3]

0

0.2

0.4

0.6

0.8

1

1.2Criterion III - I341

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

time

x 107

0

0.2

0.4

0.6

0.8

1


0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

0

0.2

0.4

0.6

0.8

1


0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

0

0.2

0.4

0.6

0.8

1


0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Ammonium pollutopragh over evaluation period (Scenarion B - case study Vils / UCO 0-0)

Criterion III for diff. locations

0

0.2

0.4

0.6

0.8

1

1.2

Em

issi

on c

riter

ion

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Emission based evaluation of urine control options (UCO)

Criterion II

CR III

CR II

COMPARISON OF CRITERION II and III

Rai

n

1.9 2 2.1 2.2 2.3 2.4x 107

time

time

time

time

Figure 41: Comparison of immission and emission based criterion


59

5.2.3. Choosing the best control strategy (Combination of CR1 and CR2)

Having the comparison of criterion II and III in mind, it is feasible to judge on control options

by using criterion I and II only. The combined evaluation as shown earlier with scenario A –

virtual catchment – is shown in Figure 42.

0

0.2

0.4

0.6

0.8

1

1.2

Criterion I+ II

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2Control optionControl option

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

VILS-REUTTE

Figure 42: Uniform weighted criterion based on test scenario B

Again, UCO 1-1 (fixed time control with interceptive control option 1) is found to be the

preferable option.

As already explained, area wide installation of separation toilets takes a long time.

The implementation shall be carried out stepwise having an increasing number of people

participating. Thus, it is of interest how the efficiency of a control option increases with an

increasing number of households having separation toilets installed. UCO 1-1 was evaluated

for different degrees of implementation.


60

For both, the emission based criterion (criterion II) and the averaging (criterion I) are steady

decreasing if an increasing percentage of separation toilets is observed.

VILS-REUTTE

0 0.25 0.5 0.75 10

0.2

0.4

0.6

0.8

1

UCO 1-1 (fixed time control)Stepwise implementation

Percentage of separation toilets

Crit

erio

ns

Criterion ICriterion II

Figure 43: Efficiency of control strategy UCO1-1 vs. degree of implementation


61

5.3. COMPARISON OF CONTROL STRATEGIES AT SCENARIOS A AND B

The results of criterion 1 (averaging) using the control options based on test scenario B case

study Vils are likewise the results based on test scenario A virtual catchment, but the results

are on a lower level. This is caused by the fact that the ratio - ammonium load caused by rain,

which is drained by sewer pipes / dry weather ammonium load - at case study Vils (scenario

B) is less than the ratio tested on scenario B.

Also the results of the criterion 2 (emission criterion) based on the test scenario B are less

than the results of criterion 2 tested on scenario A. The control strategy 1-2 (fixed time /

interruption depends on QDR) is much more efficient at test scenario A (virtual catchment),

because the flow time between sub catchment and combined sewer overflow (CSO) is short.

Thus the flow retention in the sewer system takes less effects regarding to the maximum

effluent flow (QDR) at a CSO.

0

0.2

0.4

0.6

0.8

1

1.2

Criterion II

Control option

Emiss

ion

crite

rion

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Scenario A – virtual catchment

0

0.2

0.4

0.6

0.8

1

1.2

Criterion II

Control option

Emiss

ion

crite

rion

0-0 1-0 1-1 1-2 1-3 2-0 2-1-a/b 2-2 3-0 3-1 3-2

Scenario B – catchment Vils Reutte

Figure 44: Comparison of criterions II for scenarios A and scenario B


62

5.4. ECONOMICAL CONSIDERATIONS

In this chapter the costs for the implementation of separation toilets in the catchment Vils are

estimated. Based on the case study Vils costs for the implementation of a urine separation

system are estimated using three different cost models.

- Amortization model (including depreciation costs)

- Initial construction model

- Support model

Based on the amortization model, the depreciation costs are the total costs of implementation.

Based on the initial construction model the costs of installing each separation toilet only one

time are included. The costs of installing each separation toilet more than one time are not

included. These costs will be paid by the population.

The 3rd calculation type is based on a support model. Costs for population are not included.

Only the amortization model calculates total costs. The initial construction model and the

support model calculate costs for the operating company. Costs for population are not

included.

Having three cost models available illustrates another novel issue of the measure.

An amortization period of 40 years is assumed as basis for the calculation. Consequently, a 40

years period is considered as maximum possible time for implementation.

Particularly description of the different financing methods is provided in the following

sections.


63

5.4.1. Calculation of depreciation implementation costs

The calculation of the implementation costs of a sustainable waste water treatment by means

of urine separation is based on following boundary conditions:

o amortization period of 40 years (start time 2005)

o implementation period of 20 years (until all toilets in the case study are equipped with

separation toilets)

o operating life of a toilet (either conventional or separation toilets) is 10 years

o All toilets in the catchment Vils Reutte will be adapted. (22,600 separation toilets +

toilets which have to be renewed periodically, because the operating life ends after 10

years. Thus, beyond 10 years the number of separation toilets is doubled to

compensate the already installed toilets that have to be removed as well.)

o Only the additional costs arising for separation toilets are considered. Thus, only the

price difference between separation toilets (700 EUR today) and conventional toilets

(350 EUR) are considered. The costs for separation toilets are assumed to drop from

700 EUR today to 580 EUR after 40 years (without inflation). Between the 20th year

and 40th year, costs are considered stable at 580 EUR.

o Total number of toilets in the case study Vils to be constant (22,600 units)

o The yearly inflation rate is taken to be 0.02 [-]

In Table A 5 the additional costs to the conventional system are calculated with an amount of

31.2 million Euros by means of a depreciation.


64

5.4.2. Costs of initial construction

The costs of construction are calculated in Table A 6. Assumption is

o implementation period of 20 years

o all toilets will be adapted (22,600 separation toilets)

o Only the price difference between separation toilets (700 EUR today) and

conventional toilets (350 EUR) are considered. The costs for separation toilets are

assumed to drop from 700 EUR today to 610 EUR after 20 years (instead of 40 years

in 0, without inflation).

o inflation rate of 0.02 [-]

o Costs for population are not included.

Accordingly, the construction costs amount 8.3 million Euros.

5.4.3. Costs of implementation based on a support model

To realize the implementation a support concept is postulated and costs for the supporting

institution (e.g. the AWV-Vils) are calculated. Assumption is that the support rate granted by

the municipality rises stepwise until 75% of toilets are implemented as separation toilets.

Progressive people are open to use new technology although only little support rates are

granted. Others, also people which are interested in new technology will use it if somewhat

higher support rates are granted. The remaining population is only convincible in case the

technology is already state of the art. Based on this assumption, the support rates are

increased stepwise for different levels of implementation.

Table 9: Rates of support model Upgrade Support rate People 0 - 25% 0.20 progressive

25 - 50% 0.35 more interested 50 - 75% 0.50 interested 75 - 100% 0.00 others

Based on the case study Vils, an inflation rate of 0.02 and the assumption that all toilets in the

catchment will be adapted (22,600 separation toilets), the support costs are calculated in Table

A 7 and amount 4.6 million Euros. Costs for population are not included.

Chapter 6 Summary and Conclusions

65

5.4.4. Comparison of implementation costs

In Figure 45 the cumulative costs over the time of each implementation are plotted.

0 10 20 30 390

5

10

15

20

25

30

35x 106 Implementation costs

time [a]

EUR

(cum

ulat

ive)

Amortization model (total costs)Initial construction model (costs for operating company)Support model (costs for operating company)

Figure 45: Comparison of implementation costs

If the waste water treatment by means of urine separation is financed according to the cost

model “initial construction” or the cost model “support”, the costs will be competitive, but the

costs for population are not included. This is not only true for final cumulated costs, but as

well during the implementation period.

Annual costs per person

To qualify the costs, annual costs per person are calculated, based on an operating time of 40

years. Costs are distributed between all inhabitants (38,000 PE). Thus, it can be seen that

implementation of a separation system does not cost a fortune.

Table 10: Cumulative costs of different implementations Costs EUR/PE/a Depreciation costs 20.51 Total costs Construction costs 5.44 Costs for operating company Supporting costs 3.05 Costs for operating company

Chapter 6 Summary and Conclusions

66

6. SUMMARY AND CONCLUSIONS

In this chapter the work with CITY DRAIN, the used urine control options and the

economical considerations are reflected.

CITY DRAIN was used because one advantage of the software is the flexibility regarding the

implementation of new features. The CITY DRAIN library can be added with new blocks

from the Simulink library and with new programmed blocks. The program language is easily

to understand, thus the learning period will be short. Another advantage is the block based

modelling of urban waste water treatment systems, because the block formation in the model

regards with the formation of structures in the plan.

Considering the inherent uncertainties within the Vils-Reutte catchment, the obtained

calibration quality is “as accurate as possible”. Thus the actualities of situation are reflected

and the performance of the tested urine control strategies based on the case study Vils Reutte

could be evaluated, adequately.

The urine separation in connection with controlled urine tank emptying is an efficient strategy

to reach both goals:

1) averaging the ammonium load pollutograph

The ratio maximum / minimum ammonium overflow load changed from ca. 2 to 1.1.

2) reduce ammonium concentration peaks in rivers.

About 42% less ammonium is spilled into rivers than without control.

The reduction of the ammonium concentrations in rivers amounts about 10-15%.

The performance indicators for each goal

- averaging criterion

- emission based crtierion

- and immission based criterion

were hardly to define. In conclusion the emission based criterion was assigned as an indicator

for loads and the “immission” criterion – which serves as crosscheck of criterion I and II – for

concentrations.

The fixed time control in connection with interceptive control option 1 (depends only on

rainfall) is the best control strategy, because the results of the performance indicators are good

Chapter 7 Literature

67

and the implementation costs of this strategy are less than the costs of the fixed time control

in connection with interceptive control option 2 or 3, due to the interceptive controls that

depend on forecast or sewer data need a central station in any case.

An improvement of the best control strategy will be possible, if the fixed time control option

is not based on an equidistant emptying pattern. In times with high ammonium load peaks at

the treatment plant, more urine tank emptyings should be executed as in times with an average

ammonium load level.

The economic considerations are difficult to estimate, but the different cost models show the

order of magnitude.

Chapter 7 Literature

68

7. LITERATURE

Achleitner S., Möderl M. und Rauch W. (2006). CITY DRAIN © - an open source approach for simulation of

integrated urban drainage systems.

Achleitner S., De Toffol S., Engelhard C. und Rauch W. (2005). Model based hydropower gate operation for

mitigation of CSO impacts by means of river base flow increase. Wat.Sci.Tech, 52 (5),87-94.

Berndtsson J. C. (2005). Experiences from the implementation of a urine separation system: Goals, planning,

reality. Building and Environment, 41 (4),427-437.

Brombach H. und Fuchs S. (2003). Datenpool gemessener Verschmutzungskonzentrationen in Misch- und

Trennkanalisationen. KA - Abwasser, Abfall, 50 (4),441-450.

DeToffol S. und Rauch W. (2006). Combined systems versus separate systems - an assessment of ecological and

economical performance indicators. 7th International Conference on Urban Drainage Modelling and

the 4th International Conference on Water Sensitive Urban Design, Melbourne, Australia. 2-7 April

2006.

Grecu M. und Krajewski W. F. (2000). A large-sample investigation of statistical procedures for radar-based

short-term quantitative precipitation forecasting. Journal of Hydrology, 239 (1-4),69-84.

Hanæus J., Hellström D. und Johansson E. (1997). A study of a urine separation system in an ecological village

in Northern Sweden. Water Science and Technology, 35 (9),153-160.

Jönsson H., Stenström T.-A., Svensson J. und Sundin A. (1997). Source separated urine-nutrient and heavy-

metal content, water saving and faecal contamination. Water Science Technology, 35 (9),145-152.

Kvarnström E. und Stintzing A. R. (2005). Swedish farmers attitudes to reuse of digestion residues from

anaerobic digestion and sourcediverted urine. 3rd International Conference on Ecological Sanitation,

Durban, South Africa. 23 - 26 May 2005.

http://conference2005.ecosan.org/papers/kvarnstrom_et_al.pdf.

Larsen T. A. und Gujer W. (1996). Separate management of anthropogenic nutrient solutions. Water Science

Technology, 34 (3-4),87-94.

Lautrich R. (1980). Der Abwasserkanal. Handbuch für Planung, Ausführung und Betrieb., Parey, Hamburg,

OEWAV R19 (Draft 2003). OEWAV- Regelblatt 19: Richtlinien für die Bemessung and Gestaltung von

Regenentlastungen in Mischwasserkanaelen. Oesterreichischer Wasser- und Abfallwirschaftsverband,

Vienna, Austria.

OEWAV (2001). Benchmarking in der Siedlungswasserwirtschaft.

OEWWV R11 (1982). Richtlinien für die abwassertechnische Berechnung von Schmutz-, Regen- und

Mischwasserkanälen .

Otterpohl R., Braun U. und Oldenburg M. (2002). Innovative Technologies for Decentralised Wastewater

Management in Urban and Peri-Urban Areas. 5th Specialised Conference on Small Water and

Wastewater Treatment Systems, Istanbul-Turkey, 24-26 September 2002.

http://www2.gtz.de/ecosan/download/iwa2002-otterpohl.pdf.

Rauch W., Brockmann D., Peters I., Larsen T. A. und Gujer W. (2003). Combining urine separation with waste

design: An analysis using a stochastic model for urine production. Water Research, 37 (3),681-689.

ANNEX

1

ANNEX

Table A 1: Calibrated data of sewers between sub catchments ID l [m] tf [min] ID l [m] tf [min] ID l [m] tf [min]

0-1 1200 16 8 1579 20 18 572 3

0-2 1800 35 9 281 4 19 2590 42

0-3 1400 30 10 1835 21 20 961 15

1 3169 40 11 496 10 21 630 8

2 1863 40 12 579 14 22 529 4

3 1453 20 13 385 10 23 1421 10

4 573 12 14 1038 16 24 277 5

5 1064 9 15 325 2 25 5257 87

6 323 3 16 762 8 26 4685 78

7 684 8 17 399 6

Table A 2: Calibrated data combined sewer overflows CSO-ID V QDR [m³] [l/s]

1 800 190

2 280 325

3 134 20

RE

UT

TE

4 270 65

5 800 80

6 374 60

7* 0 0VIL

S

8 200 80

Table A 3: Calibrated data of pumping stations PS-ID Pumping Station Sewer QP Voff Von V [m³/s] [m³] [m³] [m³]

1 Klosterweg 02 0.150 18.50 2 Rieden 08 0.008 0.40 3.27 2.87 3 Musau 09 0.011 0.22 0.92 0.70 4 Roßschläg 09 0.009 0.23 0.91 0.68 5 Oberpinswang 10-1 0.011 0.40 2.71 2.31 6 Unterpinswang 10-2 0.011 0.40 2.85 2.45 R

EU

TT

E

7 Heiterwang 12 0.0110 22.15

Shaded cells are modified.

ANNEX

2

Table A 4: Calibrated data of sub catchments Village # Catchment Type PE A tf f hv,i hv,d [ha] [min] [-] [mm] [l/(s*ha)]

Pfronten Pfronten 13105 211.6 1-1 Pfronten 1 CSS 1618 26.1 30 0.10 1 1.25 1-1-1 Pfronten 1-1 SSS 2556 41.3 30 0.10 1 1.25 1-2 Pfronten 1-2 CSS 4961 80.1 28 0.10 1 1.25V

ILS

1-3 Pfronten 1-3 CSS 3970 64.1 22 0.10 1 1.25Vils 2 Vils CSS 1731 94.5 25 0.10 1 1.25Pinswang SSS 485 19.5 3 Unterpinswang 295 11.8 33 0.19 1 1.25 4 Oberpinswang 190 7.6 20 0.20 1 1.25Musau SSS 374 23.0 5 Musau 256 15.8 29 0.18 1 1.25 6 Brandstatt 90 5.5 12 0.19 1 1.25 7 Roßschläg 28 1.7 3 0.21 1 1.25Pflach SSS 1277 59.4 8 Pflach O 781 36.3 28 0.18 1 1.25 9 Pflach W 496 23.1 31 0.16 1 1.25Reutte CSS 8289 187.5 10 Reutte N 3195 72.3 24 0.15 0.8 1.25 11 Reutte SW 2959 66.9 29 0.15 0.8 1.25 12 Reutte SO 2135 48.3 30 0.15 0.8 1.25Breitenwang 2728 101.1 13 Breitenwang CSS 814 30.2 33 0.15 0.8 1.25 14 Mühl SSS 1619 60.0 40 0.16 1 1.25 15 Lähn P CSS 150 5.5 7 0.15 0.8 1.25 16 Lähn L CSS 146 5.4 4 0.15 0.8 1.25Lechaschau 2148 79.9 17-1 Nord SSS 967 36.0 21 0.19 1 1.25 17-2 Süd SSS 1181 44.0 23 0.19 1 1.25Wängle 1394 35.2 18 Wängle CSS 1355 34.2 19 0.15 0.8 1.25 19 Holz CSS 39 1.0 6 0.21 1 1.25Ehenbichl 1521 31.1 20 Ehenbichl SSS 1138 23.3 20 0.18 1 1.25 21 Rieden SSS 383 7.8 14 0.23 1 1.25Höfen 1756 66.3 22 Höfen NW SSS 481 18.2 9 0.16 1 1.25 23 Höfen NO SSS 715 27.0 28 0.16 1 1.25 24 Höfen S SSS 560 21.1 15 0.19 1 1.25

RE

UT

TE

Weissenbach 25 Weissenbach SSS 1588 52.8 55 0.21 1 1.25 Heiterwang 26 Heiterwang SSS 707 27.6 24 0.18 1 1.25 Bichelbach 27 Bichelbach SSS 1264 5.0 6 0.18 1 1.3

Shaded cells are modified.

ANNEX

3

Table A 5: Calculation of implementation costs based on a depreciation Year Toilets Price/Separation

Toilet Price/Separation

Toilet Price/Conventional

Toilet Costs

present [EUR]

future [EUR]

future [EUR]

future [EUR]

0 1130 700 700 350 3954801 1130 700 714 357 4033902 1130 700 728 364 4114583 1130 700 743 371 4196874 1130 700 758 379 4280815 1130 670 740 386 3992166 1130 670 755 394 4072007 1130 670 770 402 4153448 1130 670 785 410 4236519 1130 670 801 418 432124

10 2260 640 780 427 79888911 2260 640 796 435 81486712 2260 640 812 444 83116413 2260 640 828 453 84778714 2260 640 844 462 86474315 2260 610 821 471 79079316 2260 610 837 480 80660917 2260 610 854 490 82274118 2260 610 871 500 83919619 2260 610 889 510 85597920 2260 580 862 520 77235721 2260 580 879 530 78780422 2260 580 897 541 80356023 2260 580 915 552 81963124 2260 580 933 563 83602425 2260 580 952 574 85274426 2260 580 971 586 86979927 2260 580 990 597 88719528 2260 580 1010 609 90493929 2260 580 1030 622 92303830 2260 580 1051 634 94149931 2260 580 1072 647 96032932 2260 580 1093 660 97953533 2260 580 1115 673 99912634 2260 580 1137 686 101910835 2260 580 1160 700 103949136 2260 580 1183 714 106028037 2260 580 1207 728 108148638 2260 580 1231 743 110311639 2260 580 1256 758 1125178

31174636

ANNEX

4

Table A 6: Construction costs of implementation Year Toilets Price/Separation

Toilet Price/Separation

Toilet Support rate Costs

present [EUR] future [EUR] EUR 0 1130 700 700 350 3954801 1130 700 714 357 4033902 1130 700 728 364 4114583 1130 700 743 371 4196874 1130 700 758 379 4280815 1130 670 740 386 3992166 1130 670 755 394 4072007 1130 670 770 402 4153448 1130 670 785 410 4236519 1130 670 801 418 432124

10 1130 640 780 427 39944411 1130 640 796 435 40743312 1130 640 812 444 41558213 1130 640 828 453 42389414 1130 640 844 462 43237215 1130 610 821 471 39539616 1130 610 837 480 40330417 1130 610 854 490 41137018 1130 610 871 500 41959819 1130 610 889 510 427990

22599 8272013

Table A 7: Support model cost of implementation Year Toilets Price/Separation

ToiletPrice/Separation

ToiletSupport

rate Costs

present [EUR] future [EUR] EUR 0 1130 700 700 0.2 1581921 1130 700 714 0.2 1613562 1130 700 728 0.2 1645833 1130 700 743 0.2 1678754 1130 700 758 0.2 1712325 1130 670 740 0.35 2925506 1130 670 755 0.35 2984017 1130 670 770 0.35 3043698 1130 670 785 0.35 3104579 1130 670 801 0.35 316666

10 1130 640 780 0.50 44076611 1130 640 796 0.50 44958212 1130 640 812 0.50 45857313 1130 640 828 0.50 46774514 1130 640 844 0.50 47710015 1130 610 821 0.00 016 1130 610 837 0.00 017 1130 610 854 0.00 018 1130 610 871 0.00 019 1130 610 889 0.00 0

22599 4639447

ANNEX

5

VERPFLICHTUNGSERKLÄRUNG

Ich erkläre, dass ich meine Diplomarbeit selbständig verfasst habe und alle in ihr verwendeten

Unterlagen, Hilfsmittel und die zugrundegelegte Literatur genannt habe.

Ich nehme zur Kenntnis, dass auch bei auszugsweiser Veröffentlichung meiner Diplomarbeit

das Institut, an dem die Diplomarbeit ausgearbeitet wurde, und der Betreuer zu nennen sind.

Innsbruck, am

mÖderl michael friedrich - universität innsbruck · mÖderl michael friedrich sustainable waste...

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