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SW195

Dynamic Influent Generator for Alternative Wastewater Management with Urine Source

Separation

Ana Barbara Bisinella de Faria. Ph.D. LISBP, Université de Toulouse, CNRS, INRA, INSA,

Toulouse, France. INSA Toulouse, LISBP ; 135 Avenue de Rangueil ;F-31400 Toulouse,

France. ana.bisinella@veolia.com

Mathilde Besson. LISBP, Ph.D candidate, Université de Toulouse, CNRS, INRA, INSA,

Toulouse, France. INSA Toulouse, LISBP ; 135 Avenue de Rangueil ;F-31400 Toulouse,

France. (corresponding author) mbesson@insa-toulouse.fr

Aras Ahmadi. Associate Professor. LISBP, Université de Toulouse, CNRS, INRA, INSA,

Toulouse, France. INSA Toulouse, LISBP ; 135 Avenue de Rangueil ;F-31400 Toulouse,

France. ahmadi@insa-toulouse.fr

Kai M. Udert. Prof. Dr. Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600

Dübendorf, Switzerland and ETH Zürich, Institute of Environmental Engineering, 8093

Zürich, Switzerland. Postfach 611, Überlandstrasse 133, 8600 Dübendorf, Switzerland

Kai.Udert@eawag.ch

Mathieu Spérandio. Professor. LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse,

France. INSA Toulouse, LISBP ; 135 Avenue de Rangueil ;F-31400 Toulouse, France.

sperandio@insa-toulouse.fr

This document is the accepted manuscript version of the following article: Bisinella de Faria, A. B., Besson, M., Ahmadi, A., Udert, K. M., & Spérandio, M. (2020). Dynamic influent generator for alternative wastewater management with urine source separation. Journal of Sustainable Water in the Built Environment, 6(2), 04020001 (26 pp.). https://doi.org/10.1061/JSWBAY.0000904

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Abstract

The simulation of wastewater treatment plants allows obtaining predictive results when one

needs to understand, evaluate, optimize or design a plant. However, one of the bottlenecks of the

simulation feasibility is to obtain reliable and dynamic influent data. This difficulty is even more

important when alternative scenarios are considered, such as source-separated streams. The

present paper offers an influent generator to simulate scenarios where urine is separated at source

and at a user-specified level of retention. The proposed tool contains several blocks to include

different contribution (household and industrial wastewater) and, due to its flexibility, allows the

user to easily modify the parameters to fit other case studies. The tool allowed generating

dynamic, long-term and predictive data for both urine and wastewater streams. Also, the

extensive set of state variables ensured the generation of influents for different modeling

platforms.

Keywords: Urine source separation; Influent generator; Phenomenological model; Dynamic

influent

1. Introduction

Wastewater treatment plants (WWTPs) are a complex combination of biological, chemical and

physical processes that shall remove pollutants from wastewaters. Given the complexity of the

system and the interaction between a large set of parameters, modelling and simulation allow, (i)

the understanding of involved processes, together with (ii) the performance evaluation and test of

control strategies, (iii) the design verification of new treatment approaches and (iv) the

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optimization of such processes. However, a predictive and robust model might not offer realistic

results if input data are incomplete or inaccurate.

In this sense, one of the limitations when considering the use of modelling is the scarce datasets

available as it is a costly and a laborious task to obtain experimental data for long-term dynamic

influent entering WWTPs (Martin and Vanrolleghem, 2014; Rieger et al., 2010). Most of the

time measuring the flowrate is not sufficient. Only on-line measurements can provide dynamic,

reliable and long-term input for simulation with a high cost associated (Gernaey et al., 2011).

An influent generator which numerically creates a dataset of influent characteristics may be used

for several purposes as highlighted by Martin and Vanrolleghem (2014). Firstly, the influent

generator can characterize an uncomplete dataset. Secondly, the generator can help to fractionate

the available measured composite variables (total chemical oxygen demand (COD), nitrogen or

phosphorus) into state variables. A state variable is a fraction of a composite variable according

to the physical state (soluble, particulate or even colloidal) and biological state (biodegradable or

non-degradable). Indeed, many modeling tools require these information. Finally, if the dataset is

completed, the influent generator can help to create a derived dataset and to assess the

uncertainty of the model response to hypothetical situations, such as temperature change,

population growth in a catchment area, storm events, or even unconventional wastewater

management options.

To respond to the first situation, three approaches are proposed. The first approach is to construct

databases, which consist of experimental data used to complete or generate similar influent loads

and flows. The second approach consists of using harmonic functions to describe the dynamic

profile of wastewater streams. Following this idea, Langergraber et al. (2008) used a 2nd order

Fourier series to propose realistic patterns for flow and composite variables based on the mixture

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of the main wastewater streams (infiltration water, urine with flush water and domestic

wastewater without urine). The third approach is based on phenomenological modelling that

attempts to integrate knowledge when describing empirically the relationship of different

observed phenomena. Martin and Vanrolleghem (2014) considered this approach as a promising

research area since it is possible to integrate knowledge about generating mechanisms and should

be improved in order to take into account both information about the catchment area and

stochastic behavior of inputs. Gernaey et al. (2011) proposed a dynamic influent generator while

adopting the above-mentioned approach and took into account all flow rate generation model,

concentration generation model, temperature generation model, first flush effect (flushing of the

sewer system after rain events) and transport in sewer model.

Recently, conventional WWTPs in which pollutants are only eliminated, are turned into recovery

facilities where pollutants are regarded as resources, and ongoing experiments are conducted to

achieve a more decentralized management of wastewater. Among non-conventional wastewater

management strategies that are nowadays gaining more interest as they may lead to more

sustainable sanitation practices, urine source separation is a particularly promising approach.

This interest is due to urine’s high concentration in nitrogen and phosphorus, whereby 75% of

nitrogen and 50% of phosphorus entering domestic WWTPs come from urine (Larsen and Gujer,

1996). When not separated at the source, these nutrients need to be treated in the WWTP and

thus are responsible for high energy consumption for nitrification and high dosages of chemical

products, such as coagulants for chemical precipitation of phosphorus and COD addition for

denitrification.

The effect of combining urine separation and wastewater design was presented by Rauch et al.

(2003). The authors provided a stochastic model that was applied to a virtual case study in order

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to understand the gains on WWTP load related to peak shaving and on the aquatic environment

with the reduction of combined sewer overflows. Rauch et al. (2003) highlighted, an interesting

approach where urine is separated, stored and released into the sewer following an integrated

strategy in order to adjust the nutrients input into the WWTP. The advantages of the application

of this strategy would be not only the control of nitrogen input into the plant but also the

avoidance of sewer overflow with urine, which might have a harmful effect on water bodies. In

addition, WWTPs are usually designed to deal with ammonia peak loads, and urine source

separation can decrease the nutrients load in the influent wastewater and then the maximum

concentration of nitrogen in the effluent. These shaving peaks would increase nitrogen treatment

stability of existing plants and would reduce the footprint of newly designed plants.

Urine can be treated separately and several options were reported in the literature, which depend

on the desired end-product and the collection setup. Urine is indeed a complex fluid composed of

several substances presenting a high variance (Rose et al., 2015 and Table S1 to S4 in

Supplementary materials) and several spontaneous processes might occur during storage and

transport. During storage, all urea is degraded and almost all nitrogen is available as ammonia,

only hours to few days is sufficient in real-world urine-separating systems to hydrolyze all urea

according to Udert et al., (2003a). Furthermore, the pH increases rapidly, almost all calcium and

magnesium are precipitated and organic compounds are converted during anaerobic processes

(Udert et al., 2006).

Harder et al. (2019) showed that two major research pathways are studied for urine treatment: i)

stabilization, water extraction and contamination reduction and ii) nutrient extraction. Different

treatments can then have several purposes to fulfill these pathways according to Maurer et al.

(2006), such as hygienisation, volume reduction, stabilization, recovery of nitrogen and

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phosphorus, nutrient removal and micropollutants removal. Moreover, the involved processes

will be different with respect to the condition of the urine that means whether it is fresh or was

already subject to urea hydrolysis and anaerobic organics conversion. While stabilizing fresh

urine will avoid nitrogen loss due to ammonia volatilization, this process needs to be achieved as

close to the user interface as possible to prevent urea hydrolysis. In this sense, some projects

worked on urine treatment or stabilization integrated into toilets or urinals (Boyer et al., 2014;

Flanagan et Randall, 2018). By treating stored urine, some nitrogen loss can occur during

collection. Nevertheless, in setups with multiple toilets and urinals, central storage and treatment

of urine can be economically and technically advantageous.

Before evaluating any treatment options it is necessary to characterize the urine quality and

quantity well. As mentioned above, one might consider data collection, yet, important sources of

data noise and variation are present in the system ranging from efficiently operating source

separation toilets and to dietary habits of toilet users and thus recovery of these data can contain

several assumptive conditions. This is especially true for source separation systems implemented

at a small scale. The current implementation scale might be too small to obtain a real influent

change at WWTP entrance.

The present paper aims to propose a dynamic influent generator based on Gernaey et al. (2011)

that takes into account urine source separation in cities with a urine retention percentage that is

easily modifiable by the user. The urine is assumed to be collected and stored before transport or

treatment. Therefore this influent generator cannot be used for fresh urine treatment. The model

generation allows obtaining two different flows: (i) the urine dynamically produced per person

and (ii) the mainstream influent for WWTPs that is directly influenced by urine separation.

Furthermore, the proposed influent generator aims to obtain not only composite variables (total

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COD, total Kjeldahl nitrogen TKN, total phosphorus TP), but also a detailed characterization of

both flows into several state variables as defined previously.

The remaining of this paper is organized as follows: Firstly, each model block will be described

along with its case specific general settings and assumptions. Secondly the inflow to WWTP

with 50% urine retention level will be analyzed. Thirdly, the flexibility of the tool will be

demonstrated by WWTP simulation with two modelling approaches. Finally, the effect of the

urine retention level will be discussed based on the operational change in the WWTP.

2. Influent generator adaption for urine source separation

2.1. General overview

As discussed previously, this study is based on the original phenomenological influent generator

from Gernaey et al. (2011). This influent generator has the advantage of being a flexible tool that

can be easily modified and that it is implemented as open-source. The tool was developed using

the Matlab® 7.0 Simulink toolbox (version R2012a). Model blocks that have not been changed in

this modified version will not be detailed here. Moreover, the proposed modifications in the code

keep the flexibility idea originally proposed by Gernaey et al. (2011) in order to be used for other

case studies.

As mentioned before and in Figure 1 the generator intends to create urine composition which is

supposed to be collected in the building and stored before transportation to the treatment plant if

necessary. Figure 2 presents a general overview of the modified influent generator. Four main

streams have to be defined here: (i) TWW stream corresponds to the Total WasteWater stream

(household and other contributors) without any urine separation; (ii) TU stream consists of Total

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Urine produced by the specified population (the same as a retention of 100% of non-diluted

urine); (iii) USC stream corresponds to the user-specified Urine Stored and Collected which is

diluted in the new specified flush; (iv) WW stream represents the influent entering the

WasteWater treatment plant (contribution from industry, rainfall, infiltration and households)

without the urine retained (previously specified). This last stream (WW) results from the

subtraction of a conventional total wastewater stream (TWW) from the separated urine stream

(USC).

As showed in Figure 2, the influent generator is structured in three main sections: the general

settings (user input), the WW Generator and the USC Generator. First, the user has to set the

general parameters according to the case study. Among the available settings, the most important

ones are the percentage of urine retention and the size of the catchment. The remaining

parameters are pre-calibrated by the authors using the available literature and only have to be

changed in very specific scenarios. It concerns the flush water volumes in case of no separation

(Old flush water), and the flush water volume for urine source separated toilet (New flush water).

The composite variables of TWW and TU as well as their fractionations are also pre-calibrated.

As mentioned previously composite variables are the commonly measured variable which

represent the total amount of one type of component (total nitrogen, total COD...). State variable

as defined as a fraction of a composite variable according to the physical state (soluble,

particulate or even colloidal) and biological state (biodegradable or non-degradable).

The WW generator is composed of five main parts: (i) Flowrate generation in households

(without retained part of urine), flowrate generation in industries, seasonal infiltration (due to

changes in groundwater level during the year) and rain generation (block A in Figure 2); (ii)

Wastewater compounds generation in households (without retained part of urine) and industries

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(block C in Figure 2); (iii) WW influent fractionation into a complete set of state variables

(comprising also temperature profile generation) (block E in Figure 2); (iv) Application of a first

flush effect in sewer (block G in Figure 2) and (v) Transport in sewers, responsible for

smoothing concentration peaks depending on the size of the sewer (block H in Figure 2).

On the other hand, the USC generator is composed of three main parts only: (i) Urine flowrate

generation (block B in Figure 2); (ii) Urine compounds generation (block D in Figure 2) and (iii)

Urine fractionation into a complete set of state variables (block F in Figure 2). First flush and

transport in sewer blocks are not present in USC generator section as it is considered urine is

stored after generation and transported by trucks.

Details of each block will be given in the subsections below together with its main assumptions.

However, blocks after fractionation (first flush effect and the sewer transport) (Figure 2) will not

be discussed as they were not modified from the original model (Gernaey et al., 2011).

Temperature profile of generated urine was considered identical to conventional generated

influent since a relatively long period of storage is proposed and is followed by a transport to the

WWTP. Finally, in order to easily explain the calculations done by the influent generator, a case

scenario will be presented as an example in our study with the following characteristics: Urine is

retained at a percentage of 50% and the catchment size is of 100,000 population equivalent (PE)

for domestic contribution only, without any industrial contribution.

2.2. Flow generation

Dynamics in flow generation consider all daily, weekly and yearly profiles. The chosen

normalized profiles for flows generated in households are showed in Figure 3A for both

wastewater and urine retained. This choice of using similar flowrate profiles for the USC and

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WW is based on the approach of Langergraber et al. (2008) that used a profile from a Fourier

series for both domestic influent without urine and for the urine flow.

As showed in Figure 3A, both USC and WW profiles followed a similar profile with comparable

maximum and minimum values. However, a slight delay is present in WW stream compared to

USC, since normally flow peaks for the non-urine flowrates will be generated after pollutants in

household, and consequently after urine generation. In addition, it is important to highlight that

the peaks correspond to the diurnal human activity hours, morning peak by 7-8 a.m. and

afternoon peak by 4-6 p.m. Considering weekly and holiday effects, those were considered to be

the same for WW and USC streams as they represent the “non-generation” of total wastewater in

the household during these periods (reduction of 8% on Saturdays, 12% on Sundays and from

12-25% on holidays) according to Gernaey et al., (2011). The considered total wastewater

flowrate (TWW) from households is 150 L.PE-1.d-1, and the urine flowrate is considered to be

1.36 L.PE-1.d-1 according to a review on the published literature realized by the authors (data

presented in the Supplementary Materials, Table S1 to Table S4). All the constituent names

followed the standardized notation (Corominas et al., 2010). Moreover, in conventional toilets

(without urine separation), the volume of water used per flush was considered to be of 5 L while

0.15 L of water per flush are considered in urine diverting toilets. Furthermore, it is considered

that each person flushes the toilet after urinating 5 times a day (STOWA, 2002). Friedler et al.,

(1996) reported an average of 3.4 ±2.0 flushes per person per day for urinating, 1.1 ±1.1 flushes

per person per day for both flushed urine and feces, and 0.08 ±0.17 flush per person per day for

feces only. However Rose et al., (2015) reported more variating data: 5.4 urinations per day in a

boy’s prison in Thailand (Schouw et al. 2002); 6 urinations/24 hr (range of 2–11 urinations/24 hr)

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in a study of children aged 6–12 years (Bael et al., 2007); and 8 urinations/day was recorded for

a population sample in the United States (n = 17) (Clare et al., 2009).

Finally, as showed in Figure 2, the total flow entering the wastewater treatment plant is

composed not only of the household and the industry contributions, but rain and seasonal

variation of groundwater level are also included. The total flowrate entering the WWTP (WW) is

thus obtained by the sum of previously described contributors, while urine flowrate is obtained

directly from urine model block. Taking into account the presented assumptions, a summary of

the inputs and the calculated values for the average flowrates is given in Table 1 with the

proposed case study of 50% urine retention as example.

2.3. Wastewater components generation

2.3.1. General aspects

When considering the urine source separation in households, the most reliable way to determine

the resulting load of components present in each stream is to consider the total quantity of these

components produced per population equivalent (PE) in a period of time (including urine and

other contributors), as the average non-dynamic value and the values for pure urine are well

characterized in literature. Therefore, the generation of components followed some main steps:

First, composite variables were specified for the total wastewater (TWW stream) and for the total

urine stream (TU) considering that the total load of components is contained in the produced

urine (retention of 100%). Following, according to the specified urine retention, new loads (of

composite variables) are calculated for the domestic wastewater with urine separated (WW) and

for the urine retained (USC). Finally, considering the total well known ratios for the fractionation

of TWW and TU in the recalculation of the new fractionation, a novel fractionation was applied

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to final streams (WW and USC, Figure 2, blocks E and F). However, considering that urine is

only diluted in the household, fractionation values of TU and USC are the similar, yet the same

assumption is not valid for TWW and WW.

Finally, the industrial contribution is not extensively described since only slight modifications

were made considering the original influent generator (Gernaey et al., 2011), such as new values

of components per PE.d-1 and phosphorus profiles.

2.3.2. Composite variables

The considered initial composite variables (soluble COD (sCOD), particulate COD (pCOD),

TKN, Ammonium (NH4+), TP and Phosphate (PO4

2-) are predefined in the generator from the

literature, which make them adaptable for other case studies. Considering the total wastewater

without any urine retention (TWW), it was decided to base all calculations on the load of total

COD, which range between 25 and 200gCOD.PE-1.d-1 and were fixed at 120 gCOD.PE-1.d-1 in

this study (Henze and Comeau, 2008). However, these values vary depending on geographical

location and personal lifestyle, and may be adapted for other case studies. Additionally, ratios

that are well defined in the literature (Pons et al., 2004; Tchobanoglous et al., 2003; Henze and

Comeau, 2008) were applied: Total nitrogen/Total phosphorus = 6, Total COD/Total nitrogen = 9

and consequently Total COD/Total phosphorus = 54. Furthermore, other ratios were defined such

as 21% of soluble COD among total COD, 75% of ammonium nitrogen in TKN, 54% of soluble

phosphate in total phosphorus and no presence of nitrite or nitrate.

From a literature compilation, unlike TWW, TU seemed to be less sensible to total loads than to

ratios between variables. Accordingly, for a separation of 100% of urine, the model was fulfilled

directly with loads per PE per day.

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Afterwards, composite variables were calculated for the specified urine retention as described

previously. A summary of input values as well as calculated ones for a 50% urine retention are

given in Table 1. It has to be noted that the composition of urine depends directly on its storage

time. Certainly, the composition of fresh urine changes as biological and chemical activities

occur during storage. Two main phenomena are observed: hydrolysis of urea into ammonium and

precipitation of phosphorus with heavy metals. The first one appears even when urine is diluted

by toilet flush, while precipitation depends on pH and then on dilution rate (Udert et al., 2003b).

As the urine composition is used in this influent generator, for both calculation of wastewater

without urine stream and for urine separated stream, we need to consider the composition of

hydrolyzed urine only, without the precipitated fraction. Indeed, when the total phosphorus

concentration in collected urine will be removed from the concentration of total phosphorus in

wastewater, if precipitation is considered, an overestimation of the remaining total phosphorus in

wastewater will be made. This is also related to the fact that for most simulators, the biological

activity responsible for urea hydrolysis is not considered; however, chemical precipitation is

sometimes included.

2.3.3. Dynamic profiles

Considering dynamics, compounds profile followed similar assumptions of flowrate profile

variation. As the most important part of human wastewater generation is expected to happen

more or less by the same time of morning and afternoon urine peaks, the same profile for WW

and USC in the generation of compounds was applied. Figure 3B shows the profile for

ammonium flux profile expressed in terms of variation from the average value (peaks are

superposed in Figure 3B). Dynamic profiles for other composite variables followed a similar

profile and can be found in Supplementary Materials (Figure S1). Profiles for total phosphorus

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and phosphate (that are not originally used on influent generator) were generated following the

same profile of TKN and ammonium respectively.

Finally, it has also to be noticed that urine has exactly the same profile for flowrate and

ammonium flux. This choice is mainly linked to the fact that urine is well represented by

ammonium flux and that, as it is a human stream, it is expected to have variable flows with a

more constant concentration in components.

2.3.4. Fractionation into state variables

As discussed previously, the modified version of the influent generator considers an extensive

list of state variables: in general, variables are considered to be divided into soluble (S), colloidal

(C) and particulate (X); also, biodegradable (subscript B) and non-degradable (subscript U)

conditions are distinguished. A complete list of considered state variables with their descriptions

is given in Table 2, , origin from composite variables, the proposed predefined values for the

total streams (TWW and TU) and the specific urine retention case (50% retention – represented

by WW and USC) that will be discussed below.

In this study, it was chosen to use a refined fractionation in order to achieve flexibility between

several platforms and the available models. Following, in case of a simulation using simpler state

variables (such as when using ASM (activated sludge model) family models), state variables only

have to be regrouped by the user in function of the model variables. In addition, Table 2 shows

that other species (total inorganic carbon, calcium, magnesium, sodium and chloride) are also

taken into account during the fractionation. This is especially important as urine can be treated

by several physicochemical processes (Maurer et al., 2006) and adding ionic species is

mandatory for process simulation.

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The fractionation values for the total wastewater (TWW) and total urine (TU) are illustrated in

the Sankey graphics in Figures S2 to S5 for total urine and total wastewater and for COD, N and

P fractionation For instance, starting from a total value of 100, variables are divided depending

on their (i) physical state (soluble, colloidal or particulate), (ii) state variable and (iii)

degradability state (biodegradable or non-degradable). As previously discussed, fractionation

values are established for total wastewater (TWW) and total urine (TU) from the available

literature and thus are calculated for the two output streams (WW and USC). Supplementary

material Figure S6 details the fractionation block, which is fulfilled with composite variables to

generate state variables.

For instance, considering the COD fractionation, composite variables already considered total,

soluble and particulate COD. Accordingly, in order to start the fractionation, a total non-

degradable part and a colloidal part were defined. Following, for the particulate part, ratios were

applied to ordinary heterotrophic organisms (XOHO), particulate inert endogenous products (XE)

and particulate inert organic matter (XU). Thus, particulate and biodegradable organic matter

(XB) can be calculated by subtracting the total particulate COD from the previously calculated

particular species. Similarly, for the soluble and colloidal parts, well defined ratios were applied

to volatile fatty acids (SVFA), methanol (SMEOL), colloidal biodegradable organic matter (CB) and

colloidal non-degradable organic matter (CU). Following, considering the total non-degradable

part in total COD previously described, soluble non-degradable organic matter (SU) was obtained

by calculating the difference between XU and CU (previously calculated). Finally, soluble

biodegradable organic matter (SB) is calculated by the subtraction to the total soluble COD.

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Similar assumptions were made for nitrogen and phosphorus where SNU and SPU were obtained

by calculating the difference between non-degradable N and P; and SNB and SPB were obtained

by subtracting the resulting total organic N and P respectively.

In order to define the fractionation values for urine, assumptions were made based on previous

studies and are listed as follows:

Even if stored urine is considered, precipitation was not included. Indeed, when the urine is

subtracted from total wastewater (TWW) to produce WW without urine separated, the

whole phosphorus is considered, as no struvite precipitation will occur in conventional

sewer. Moreover, the precipitation of struvite can be considered separately with its

corresponding kinetics in a simulator when using the influent generated. This way, the

precipitates are supposed to be emptied simultaneously as urine from storage tank and

transported to the treatment plant where struvite could be recovered;

For urine (assumption of stored urine), 7% of the total COD is particulate (and colloidal)

and thus 93% is considered to be soluble (Udert et al., 2013);

57% of soluble COD in urine is considered as VFAs (Udert et al., 2013) when stored urine

is considered;

85% of COD in urine is considered to be easily biodegradable (Udert et al., 2006), an non-

degradable fraction of 9% is considered similar to the URWARE model from (Jönsson et

al., 2005);

The ratio between particulate in colloidal and particulate is 75% (𝑋𝑈

𝑋𝑈+𝐶𝑈)and the

assumption of 75% (𝑋𝐵

𝑋𝐵+𝐶𝐵) of biodegradable is applied to colloidal and particulate.The

same assumption is considered in the wastewater (Henze et Harremoës, 1992);

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Ammonium in urine is considered to be 94% of total nitrogen which is in accordance with

values proposed by Udert et al. (2006) and STOWA (2002);

Soluble phosphate is considered to be 95% of total phosphorus (Udert et al., 2006) (as no

precipitation is considered at this stage, it is the fresh urine characteristics);

A fraction of 80% of soluble was applied to organic nitrogen and phosphorus without

ammonium and phosphate, respectively;

Colloidal and particulate non-degradable nitrogen and phosphorus are negligible in urine

and thus are not considered.

Regarding ionic species, total inorganic carbon, calcium, magnesium, strong cations and strong

anions (represented respectively by SCO2, SCa, SMg, SNa, SCl in Table 2) were considered following

a ratio to SNHx (Table 3) and were later checked by electro neutrality in the SUMO simulator to

verify if pH and alkalinity were consistent with real world values (USC: pH=9.17, Alkalinity=0.5

eq.L-1; WW: pH=7.79, Alkalinity=0.0050 eq.L-1). The simulations took into account the total

urine (TU) and wastewater (TWW) compositions from Table 2. The urine from Table 2 is not

precipitated, but in the SUMO simulator the fresh urine leads to 30% decrease in orthophosphate

due to struvite precipitation. The final magnesium concentration (14.3 mgMg.L-1) is in accordance

with literature (see Supplementary materials Table S4).

The choice of using ammonium ion concentration profile (Figure 3B) in order to determine

alkalinity and ions concentration profiles comes mainly from the fact that (i) in the case of urine,

bicarbonate will emerge from urea hydrolysis (that will also generate SNHx) and (ii) a large part

(29%) and a minor part (8%) of alkalinity consists of ammonia and phosphate compounds,

respectively. It has to be noticed that, even if in some models ionic species will not be used, it is

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important to calculate them as the most part of urine treatment technologies depend on pH or

specific ionic species (Maurer et al., 2006).

Other general setting parameters consider the assumption from BSM2 (Benchmark Simulation

Model n°2 Jeppsson et al, 2007) that total suspended solids (TSS) are equal to 75% of the total

COD and the conventional ratios between COD and Volatile Suspended Solids (VSS) are equal

to 1.42 for biomass,1.8 for XB and 1.3 for XU. Following the last assumptions, inorganic

suspended solids (ISS) can be calculated (in the end, after sewer), by the difference between TSS

and VSS.

2.4. Noise addition

In order to add realistic conditions to the generated flows and avoid direct correlation between

variables, noise (controlled random variation) was added to flowrates (domestic, industrial and

rain flowrates) as well as to composite and state variables. This was done following the approach

of Gernaey et al. (2011): a zero mean white noise is added using the random number block of

Simulink that outputs a Gaussian distributed random signal. Attention was paid in order to select

different seeds for each noise added and variance was calculated using a variation factor

specified by the user that is multiplied to the average value and squared. Thus, this specified

factor might be comparable to the percentage standard deviation. Also, the considered sampling

time is of 15 min, in this way 4 points per hour were created. Considered values for noise factors

are presented in Table 4, which can be easily modified in the influent generator code in order to

represent other case studies. It is important to note that, in case of urine, the noise addition is an

attempt to take into account, not only urine generation fluctuations but also urine recovery

variance.

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Furthermore, following the addition of noise values, saturation blocks were maintained in order

to limit the range of obtained values by fixing a lower and upper bound. It has to be noticed that

the chosen values for noise are related to the fact that flowrate, composite variables (flux) and

state variables (concentration) from household are supposed to be more influenced by noise

variation than urine and industry. This variation is not anymore linked to daily, weekly or yearly

profile; however it represents the noisy variation of values. Though, it has to be noticed that

when considering the urine transport by truck to the plant, the user might add a storage tank and

thus, daily dynamics will no longer play an important role.

3. Example of simulations obtained with the generated influent

The use of the influent generator was analyzed in three different steps. The first one is the

analysis of the case study with 50% of urine retention level with the influent at the entrance of

WWTP and urine. The second simulation aims to describe the flexibility of the tool by

comparing the generated influent arriving to a WWTP with two modelling approaches ASM1

(Activated Sludge model 1) and the plant-wide model Sumo1 from SUMO (Sumo15 version beta

69.1) (Dynamita, 2016). The case of 50% of urine retention was considered for both models. The

last simulation consists of the comparison of different urine retention percentages on the WWTP

operation cost.

3.1. Case studies

For the three simulated influents were dynamically generated considering a 100,000PE city.

In the last two simulations, only the water line of the WWTP was simulated in order to obtain the

impact on the most important WWTP parameters that might be influenced by the urine

separation. The simulated WWTP consists of a MLE (Modified Ludzack-Ettinger) process with

20

2 anoxic tanks (total volume of 3000 m3) and 3 aerobic tanks (total volume of 9000 m3 with

fixed dissolved oxygen of 2 g.m-3) similarly to BSM1 (Benchmark simulation model 1). Also, a

secondary clarifier with fixed solids removal efficiency was simulated, the internal recycle rate

was set to approximately 300% of influent flowrate and the sludge retention time was fixed to

approximately 15 days. The simulator was fed with the influents using the Sumo1 plant-wide

model, and an initialization with the corresponding non-dynamic influent for each case study was

performed until steady state was reached. Afterwards, the simulation was conducted dynamically

for a period of 14 days. The last 7 days will be discussed.

3.2. Comparison of modeling approach and urine retention level

It has to be noticed that as the ASM1 model does not consider either non-degradable nitrogen or

phosphorus species, consequently they were not added to the adapted ASM1 influent. In order to

obtain ASM1 corresponding influents, state variables were adapted as showed in Table 5.

Additionally, in order to be comparable, kinetic and stoichiometric Sumo1 parameters were

modified to be in accordance with those from ASM1. Furthermore, since fermentation is not

included in ASM1 model, the fermentation rate in Sumo 1 is set to zero.

Finally, when considering different levels of urine retention, percentages of 0% (no urine

separation), 20%, 50%, 80% and 100% (all urine is retained) were studied. For these two

comparisons, several operational parameters were studied, such as ammonium and nitrate

concentrations in effluent and the air flowrate in the aerated tank. This way, a first view of the

benefits of source separation on the WWTP can be highlighted.

4. Results and discussion

21

4.1. Average results

In this first step, the dynamic results will be analyzed by calculating the reduction in the average

load between the two scenarios, without and with urine source separation (Figure 4). First, as

expected, the effect of recovering 50% of the urine does not influence mainly the new flowrate

entering the WWTP, as it is only a reduction of 6% of total domestic wastewater production.

However, the effect in state variables is substantial. The most important reductions are to be

considered, especially regarding soluble COD (mainly VFA - 49%), total nitrogen (39%) and

total phosphorus (19%). Considering N reduced species, important abatements are obtained in

ammonia (49%), as urine is the main contributor of this compound entering the WWTP.

However, the biodegradable (SNB) and the non-degradable (SNU) soluble organic forms of

nitrogen are also well reduced (by 46% and 48% respectively). However, the non-degradable

soluble organic forms (SNB) of nitrogen represent only 0.6% of total nitrogen in TWW (from

Table 2). Following, phosphorus species reductions are mainly due to soluble phosphate (35%).

According to Larsen and Gujer (1996), urine contributes to 75% of TKN and 50% of TP in

wastewater. From the literature review the value is slightly different with 80% of TKN and 40%

of TP coming from urine. This is mainly due to a higher load of phosphorus in the wastewater

than the one considered in Larsen and Gujer (1996). The phosphorus content in wastewater

depends on the use of phosphate in detergent which can lead to this difference. However, thanks

to the flexibility of the tool, the phosphorus load in wastewater can be adapted to each situation.

4.2. Daily and weekly profiles

The obtained dynamic profiles for both wastewater input (WW) and urine stream (USC) for the

simulated case of 50% of urine retention are showed in Figure 5. Also, the average value of each

22

variable is showed in colored boxes together with its coefficient of variation (percentage), And

the daily volatile fatty acid variation are presented in Figure S7 in Supplementary Materials.

When comparing any of the presented profiles for USC and WW in Figure 5, it can be seen that

the variance of WW flowrate is smoothened in relation to urine stream. The wastewater profile

corresponds indeed at the entrance of WWTP, the size of the sewer will induce a delay between

production and collection. On the contrary, the USC corresponds to urine at source without any

sewer effect. Also, as discussed previously, a small delay is present in WW flowrate in relation to

USC flowrate profile. This delay represents the generation of excreta in the morning before the

dilution by wastewater coming from other sources (showering, dishes…) In addition, when

analyzing concentrations, the dynamic profile is markedly present for all compounds in WW

stream (only ammonia in figure 5 and volatile fatty acid in Figure S7 presented), with the

morning and afternoon peak for daily dynamics and the weekend effect for the last two days of

the week. However, this effect is less evident in urine concentration. This is in accordance with

the assumption described previously (paragraph 2.3.3) that concentrations of compounds in urine

are not assumed to vary importantly during the day. This assumption can also be validated by the

coefficient of variation that is more than two times less important for the urine stream. Finally,

the addition of noise achieved the effect of decreasing the correlation between variables and thus

the generated influent represents better real life influent streams.

4.3. Validation of the generated influent for different modelling approaches

According to the flexibility idea of the influent generator, general results for the simulation using

the plant-wide model Sumo1 are proposed in Figure 6 together with the ones generated using

ASM1. Results for the considered operational parameter, airflow input, showed to be very

similar for both models. Even when calibrated, effluent outputs present a small difference that is

23

explained by the different model approach itself. As it can be noticed in Figure 6, peaks of

aeration are smoothened in Sumo1; however, the average result is similar. The nitrate/nitrite

profile is lower with ASM1 model. Furthermore, results showed that the generated influent might

be used for both platforms. In the case of ASM1 model, a sum of the originally proposed state

variables in the influent generator is required in order to obtain the correct input variables.

Finally, none of the discussed simulations were sensitive to either phosphorus treatment or ionic

species. Indeed ASM1 does not consider those species and the processes which could be

sensitive to those species were not considered in simulations with sumo1 model as well.

Basically, the addition of phosphorus removal modelling would be more sensitive to ionic

species and this could be the case of future extended simulations.

4.4. Effect of urine retention levels

Comparison results between different urine retention percentages are showed in Figure 7.

Scenarios consisting of no urine retention, 20%, 50%, 80% and the total urine retention in

households were compared. Results showed that even the retention of only 20% of urine is

already capable of shaving the effluent ammonia peaks substantially (Figure 7A). When

increasing the urine retention and achieving 80%-100% of separation at source, the distribution

of ammonia output in the WWTP is almost smoothened without any more peaks. An important

reduction in air flowrate (Figure 7B) due to reduction of oxygen needed for nitrification was also

noticed as a consequence of reducing the ammonia input peaks. These results have not been

observed before as the previous studies focused on steady state results. Jimenez et al., (2015)

showed a continuous small increase of ammonium concentration in effluent with urine retention

level because of a decrease of nitrifying bacteria, thus when taking into account these dynamics,

this trend was no longer observable.

24

Finally, NOx (nitrate and nitrite) output (Figure 7C) is also markedly reduced and the differences

between the minimum and the peak value are importantly reduced. These results lead to two

major consequences: more stable outputs in the plant could be achieved and potentially, the size

of the WWTP can be reduced with different process configuration as studied by Wilsenach et van

Loosdrecht, (2006).

5. Conclusions

Compared to traditional sampling campaigns, influent generators are non-expensive, elegant and

little time consuming tools used to obtain input data for modelling and simulation. This is

especially the case when non-conventional modifications of the WWTP need to be evaluated and

thus there are not any real world installation available where measurements could be made. The

use of numerical influent generators allows easily obtaining complete datasets even when

complex fractionation is required, while also allowing the predictive simulation of condition

changes and of dynamic aspects.

The proposed modified phenomenological influent generator is fit for the case of urine source

separation, considering urine that was collected and stored in tanks, and where ammonium is the

main nitrogen component. This generator allows the evaluation of alternative scenarios of

wastewater management and treatment by taking into account the dynamics of production. It can

be used for evaluation by modelling of either urine treatment or WWTP operation as the urine

and the inflow of the WWTP are determined. Our results show that the tool is able to generate

dynamic, long-term and predictive data for both urine and wastewater streams. The important

benefits of urine separation are demonstrated by dynamic simulation of a typical WWTP,

25

reducing the daily nutrient peak load, improving the quality of rejected water, and reducing the

energy needs for aeration.

Moreover, due to its flexibility and the use of an extensive set of state variables, the generated

influent can be used in several modelling approaches. In this study the ASM1 model and the

Sumo1 plant- wide model were compared. In this sense, different type of treatment can be

investigated, for example biological treatment of urine via nitrification but also physicochemical

treatment as struvite precipitation.

Even if stored urine is the influent generated by the proposed set of parameters, alternatives in

wastewater management can be envisaged such as different retention levels or even other

separation scenarios, e.g.: releasing ammonia in the sewer during the night, black water

separation, unconventional greywater treatment. The tool is flexible enough and is easy to

modify the hypothesis in order to generate other streams.

The tool is now available for future simulations including innovative wastewater and urine

management scenarios, optimization of plant design and operation depending on source

separation level.

Data Availability Statement

Some or all data, models, or code used during the study were provided by a third party. It

concerns the original code of WWTP influent generator (Gernaey et al., 2011). Direct

requests for these materials may be made to the provider as indicated in the

Acknowledgments.

26

Some or all data, models, or code generated or used during the study are available from the

corresponding author by request. It concerns the modified model including urine separation

for influent generation.

Acknowledgments

The authors acknowledge both Lund University (Sweden) and the Technical University of

Denmark for providing the source code upon the study is constructed.

Ana Barbara Bisinella de Faria was a PhD student at LISBP, University of Toulouse, France

funded by the Ministry of National Education, Higher Education and Research. Mathilde Besson

is a PhD student at LISBP, University of Toulouse, France funded by the French Water Agency

(Agence de l’Eau Adour Garonne).

The authors would like to thank Dr. Gerald Matar for proofreading.

Supplemental Data

Tables. S1–S5 and Figures S1-S7 are available online in the ASCE Library

(https://ascelibrary.org).

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31

Fig. 1. Schematic overview of variable used in the influent generator and the assumed urine

collection system. (TU: Total Urine, TWW: Total Wastewater, USC: Urine Stored and Collected

and WW: Wastewater at the entrance of WWTP).

Fig. 2. General overview of the urine source separation influent generator

Adapted from Gernaey et al., 2011.

Environmental Modelling & Software, 26

General settings

Urine retention level

Size of the

catchment (PE)

Flushwater volumes

TWW composite

variables

TWW fractionation

TUS composite

variables

TUS fractionation

TWW: Total wastewater

TUS: Total pure urine

WW: Collected wastewater (without retained urine)

US: Collected urine (as function of the retention level)

A: Flowrate

(households, industry,

infiltration, rainfalls)

B: Flowrate

(urine from

households)

C: Pollutants

(households and

industry)

D: Pollutants

(urine from

households)

E: Fractionation

F: Fractionation

G: First flush

effect

H: Sewer

effect

WW Generator

US Generator

COLLECTED

URINE

TO WWTP

32

Fig. 3. Normalized profiles for Total Urine (TU) and Total WasteWater (TWW) streams. A)

Daily flow rate profile; B) Daily ammonia mass flow (load) profile (identical for both profiles).

Fig. 4. Reduction of WasteWater (WW) input variables when considering 50% urine source

separation

33

Fig. 5. Comparison of profiles between WasteWater (WW) and Urine Stored and Collected

(USC) A) Weekly flowrate profiles ; B) Daily variation SNHx.

Fig. 6. Comparison of results obtained using Sumo1 and ASM1 models for A) Effluent

ammonia, B) Air flowrate, C) Effluent nitrate and nitrite (SNOx)

34

Fig. 7. Comparison of different urine retention levels considering performance: A) Effluent

ammonia; B) operational (Air flowrate) and C) Effluent nitrate and nitrite (SNOx) results

35

Table 1. Input and calculated values for composite variables for total wastewater without urine

retention (TWW), total urine stream (TU), wastewater with urine retention (WW) and separated

urine stream (USC) (case study of 50% urine retention without considering industrial wastewater

contribution - See Table S1 to Table S4 in Supplementary Information for literature review)

Variables Description Units

Literature

Value

Calculated values based

on the specified retention

of 50%

TWW TU WW USC

Q* Flowrate l.PE-1.d-1 150 1.36 137.2 1.06

sCOD Soluble COD load gCOD.PE-1.d-1 25 10.4 19.8 5.2

pCOD

Particulate COD

load

gCOD.PE-1.d-1 95 0.78 94.6 0.4

TKN

Total Kjeldahl

nitrogen load

gN.PE-1.d-1 13.3 10.4 8.1 5.2

NH4+ Ammonium load gN.PE-1.d-1 10 9.8 5.1 4.9

TP

Total phosphorus

load

gP.PE-1.d-1 2.2 0.88 1.8 0.4

PO42-

Orthophosphate

load

gP.PE-1.d-1 1.2 0.84 0.8 0.4

NOx

Nitrite and nitrate

load

gN.PE-1.d-1 - - - -

36

Note: * The flowrate for TU comprises only pure urine (without flush water) while the other

streams already include flush water (in this case study the flush volume is different when

using source separated toilet)

37

Table 2. Detailed state variables and the corresponding considered initial values for total urine stream (TU), total wastewater without

urine retention (TWW), separated urine stream (USC) and wastewater with urine retention (WW)

Composite

variable

Physical

state

State

Variable

Description Unit

Literature values 50% urine retention

TWW TU WW USC

Total COD

sCOD

SVFA Volatile fatty acids gCOD.m-3 30.0 4355 16.2 2807

SB Readily biodegradable substrate gCOD.m-3 56.6 2628 50.9 1694

SU

Soluble non-degradable

substrate

gCOD.m-3 39.4 657.1 39.7 424

CB

Colloidal biodegradable

substrate

gCOD.m-3 81.5 122.7 86.7 79.1

pCOD

XB

Particulate biodegradable

substrate

gCOD.m-3 282.0 368.7 300. 238

CU

Colloidal non-degradable

substrate

gCOD.m-3 20.4 20.6 21.7 13.3

XU

Particulate non-degradable

substrate

gCOD.m-3 78.2 61.4 83.3 39.6

38

XOHO Ordinary heterotrophs gCOD.m-3 11.9 - 12.7 -

TKN

1 SNHx Total ammonia gN.m-3 50.0 7198.5 27.3 4640

Organic N

SNB

Soluble biodegradable organic

N

gN.m-3 2.3 318.0 1.3 204.7

CNB

Colloidal biodegradable organic

N

gN.m-3 3.3 23.0 3.4 14.8

XNB

Particulate biodegradable

organic N

gN.m-3 9.8 69.0 10.2 44.4

SNU

Soluble non-degradable organic

N

gN.m-3 0.4 54.0 0.2 34.6

CNU

Colloidal non-degradable

organic N

gN.m-3 0.2 - 0.2 -

XNU

Particulate non-degradable

organic N

gN.m-3 0.8 - 0.8 -

39

TP

2 SPO4 Orthophosphate gP.m-3 6.0 617.6 4.2 398.1

Organic P

SPB Soluble biodegradable organic P gP.m-3 0.6 22.0 0.5 14.4

CPB

Colloidal biodegradable organic

P

gP.m-3 0.8 2.0 0.9 1.0

XPB

Particulate biodegradable

organic P

gP.m-3 3.6 5.0 3.8 3.1

SPU

Soluble non-degradable organic

P

gP.m-3 0.04 1.0 0.04 0.04

CPU

Colloidal non-degradable

organic P

gP.m-3 0.02 - 0.02 -

XPU

Particulate non-degradable

organic P

gP.m-3 0.08 - 0.08 -

Other species

SCO2 Total inorganic carbon gCO2.m-3 210.0 1.13E+4 183.3 7285

SCa Calcium gCa.m-3 60.0 187 63.4 120.6

SMg Magnesium* gMg.m-3 11.5 158 11.7 102.1

40

SNa Sodium (strong cations) gNa.m-3 85. 2822 80.6 1818.8

SCl Chloride (strong anion) gCl.m-3 201.5 4067 200.5 2622

Note: 1: Ammonium

2: Soluble phosphate

* As urine is hydrolyzed but precipitation is not considered

Variables having a zero value were not included in the table (Endogenous products, other biomasses, nitrate and nitrite)

41

Table 3. Ionic species molar ratio to ammonium for total wastewater without urine retention

(TWW) and total urine stream (TU)

Variable

Ratio to SNHx

TWW TU

SCO2 4.2 1.6

SCa 1.2 0.026

SMg 0.23 0.022

SNa 1.7 0.39

SCl 4.03 0.57

42

Table 4. Noise factors considered in this study

Parameter Noise factor

Domestic WW flowrate 0.15

Industry flowrate 0.05

Urine flowrate 0.05

Domestic WW composite variables 0.1

Industry composite variables 0.1

Urine composite variables 0.05

WW state variables 0.1

Urine state variables 0.01

43

Table 5. Adapted inputs for ASM1 simulation

Description ASM1 state variables Plant-wide model

state variables

Soluble inert organic matter SI SU

Soluble biodegradable organic matter SS SVFA+SB+SMEOL

Particulate inert organic matter XI XU+CU

Particulate biodegradable organic matter XS XB+CB

Ordinary heterotrophic organisms XB,H XOHO

Nitrifying organisms (NH4 to NO3) XB,A XAOB+XNOB

Particulate inert endogenous products XP XE

Total nitrite + nitrate SNO SNO2+SNO3

Total ammonia SNH SNHx

Soluble biodegradable organic N SND SNB

Particulate biodegradable organic N XND XNB+CNB