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CIBW062 Symposium 2012

23

Modelling sustainability in water supply and drainage

with SIMDEUM® E.J. Pieterse-Quirijns (1), C.M. Agudelo-Vera (2), E.J.M. Blokker (3) 1. [email protected]

2. [email protected]

3. [email protected]

1, 3. KWR Watercycle Research Institute, Nieuwegein, The Netherlands

2. Urban Environmental Technology and Management Group, Wageningen University

and Research Centre, Wageningen, The Netherlands

Abstract Energy costs and climate change challenge the water industry to promote sustainability.

Sustainability issues for a building’s water system are saving of water, materials and

energy in the supply of water to a building, reuse of wastewater and rainwater

harvesting, heat and resources recovery from wastewater. These applications require

insight in the cold and hot water demand of a building or in the characteristics of the

drainage loads. SIMDEUM®, an end-use model to simulate residential and non-

residential cold and hot water demand patterns, can provide this information. In this

paper three successful applications of SIMDEUM for sustainability in water supply and

drainage are illustrated. First, SIMDEUM based design rules yield energy efficient

designs of water heaters. Second, SIMDEUM assists in a proper choice of storage

capacities in grey water recycling and rainwater harvesting systems. It supports

minimising urban CO2 footprint. Third, SIMDEUM is adapted to generate discharge

patterns including information on thermal energy and nutrient load, to study

possibilities to recover energy and resources from wastewater.

Keywords

SIMDEUM®, cold and hot water demand, energy efficient heater, sustainability, grey

water recycling, rainwater harvesting, energy recovery and resources, wastewater

1 Introduction

Water utilities face the challenge of becoming more energy efficient. Energy is the

highest operating cost item for most water and wastewater companies. High energy

consumption is inextricably linked to climate change. Climate change confronts the


24

water sector with the need to optimise energy use and limit greenhouse gas emissions

from their operations (Frijns et al., 2012; Frijns, 2012). Energy efficiency can be

achieved by saving energy but also by the recovery of energy from wastewater (Wanner

et al., 2005).

It is also expected that climate change will cause scarcity of water in many countries,

due to the forecast reduction in rainfall or the alteration of its regime. Population

growth, increased consumption and urbanisation will also place increased pressure on

water management. Nowadays, cities are highly dependent on external resources, while

overlooking local possibilities of self-producing resources by cascading, recycling and

recovering. For instance, rain and wastewater are seen as a nuisance and as such is

removed from cities instead of valuing its potential as a local resource to optimise the

urban water cycle. For reasons of sustainability new concepts are under development to

reuse or recycle grey water or to use rainwater. Extensive environmental benefits will

also result from a reduced demand on water resources and, where grey water is used,

reduced volumes of wastewater going to the sewer (Van Leeuwen et al., 2009;

Verstraete et al., 2009).

At the building level sustainability can refer to saving of water, materials and energy in

the supply of water to a building, to reuse or recycling of wastewater and rainwater and

to recovery of heat and resources from wastewater. To study these concepts of

sustainability, understanding the cold and hot water demand of a building on the fixture

level or in the characteristics of the drainage loads is required. This knowledge is used

for a design of installation and heater capacity based on realistic water demands to have

sustainable and energy-efficient designs. Moreover, the information on the fixture level

is needed to calculate the desired quantity of grey water for a building (for example to

flush the toilets and for irrigation) and the amount of grey water leaving the building

(from sinks, dishwasher, bath, and shower). The quantity and quality of the drainage

loads, as temperature and concentration of nutrients, is required to study the recovery of

heat and resources from water leaving the building through the sewage system.

SIMDEUM® is a model that supports this understanding. SIMDEUM stands for

"SIMulation of water Demand, an End-Use Model." It is a stochastic model based on

statistical information of end uses, including statistical data on water appliances and

users (Blokker et al., 2010). SIMDEUM’s philosophy is that people’s behaviour

regarding water use is modelled, taking into account the differences in installation and

water-using appliances. This means that in each building, whether it is residential, like a

house, or non-residential, like an office, hotel or nursing home, the characteristics of the

present water-using appliances and taps are considered as well as the water-using

behaviour of the present users. For each person, his presence is modelled and when he

uses water and for which reason. The characteristics of each appliance are defined, like

the flow rate, duration of use, frequency of use and the desired temperature. The

duration and frequency may vary depending on the users: a teenager showers more

frequently and longer than an elderly person. Moreover, the duration, frequency and the

desired temperature of an appliance depends on the type of appliance (e.g. particular

type of washing machine) and the particular application. For example, a kitchen tap can

be used for filling a glass (15 s, 0.167 l/s, 10°C) or for washing dishes (45 s, 0.25 l/s,

55°C). SIMDEUM calculates for each appliance at what time it is used, by whom and


25

for which purpose. This results in a demand pattern for cold and hot water at each

appliance. By the addition of the demand patterns of all appliances, the demand pattern

of a house, office, hotel or nursing home is obtained. The characteristics of the users and

the appliances are different for each type of building and are extensively described in

Blokker et al. (2010 and 2011). Measurements of cold and hot water patterns on a per

second base in different types of buildings show that SIMDEUM renders a reliable

prediction of both cold and hot water demand (Pieterse-Quirijns et al., 2011).

SIMDEUM’s basis gives insight in the reason for which the water is used and at what

temperature this water needs to be. Therefore, it also provides information of the

wastewater quantity, temperature and quality that will leave the building through the

sewage system (e.g. shower water at 35°C with soap residue, or toilet water at 15°C

with medicines, hormones and nitrates). In this paper, this information is applied to

transform SIMDEUM from a demand model into a discharge model.

The purpose of this paper is to illustrate with three cases the contribution of SIMDEUM

in several sustainability issues, in both supply and drainage to buildings:

1. energy efficient design of water heaters

2. grey water recycling and rain water harvesting system.

3. recovery of thermal energy and nutrients from wastewater.

2 Case I: SIMDEUM® in energy efficient design of heaters

2.1 Introduction

Existing Dutch guidelines related to the water demand of residential and non-residential

buildings are outdated and do not cover hot water demand for the appropriate selection

of hot water devices. Moreover, they generally overestimate peak demand values

required for the design of an efficient and reliable water system. Badly designed

systems can cause stagnant water with hygienic consequences, and are less energy

efficient and therefore more expensive to run.

SIMDEUM simulates the cold and hot water demand of different types of residential

and non-residential buildings in a reliable way. As an example, this is illustrated in

Figure 1 for an apartment building. Another example for a nursing home can be found

in Pieterse-Quirijns et al. (2011). Based on water demand patterns simulated by

SIMDEUM, a procedure was developed to derive design rules for peak demand values

of both cold and hot water during various time steps (Pieterse-Quirijns et al., 2010). In

this procedure, SIMDEUM simulates for each standardised building diurnal water

demand patterns, for a specific value of a dominant variable. This dominant variable

characterises the size of a building, such as the number of beds in a nursing home. The

standardisation of each type of building means that for a specific value of the dominant

variable, a building is constructed with the corresponding number of toilets, showers,

kitchen personnel, visitors, etc. From the demand patterns at different values of the

dominant variable, the maximum peak demand values for cold and hot water are

derived. It appears that these peak demand values for several buildings can be described

by simple linear relations as a function of the dominant variable. These linear relations

function as design rules. The design rules are validated with measurements of cold and


26

hot water diurnal demand patterns on a per second base for various types of buildings.

The validation shows that the design rules yield a reliable prediction of the actual water

demand, where existing guidelines and practices overestimate the water demand causing

overdimensioned systems (Pieterse-Quirijns et al., 2011). In this case study, the energy-

saving consequences for the design of heating systems are illustrated.

2.2 Capacity of heaters

The simulated patterns of hot water demand give insight into the peak demand value of

hot water, but also in the maximum hot water use in different time periods, 10 minutes,

1 hour, 2 hours and 1 day. These characteristics of hot water demand are the outcome of

SIMDEUM based design rules and can be applied in general design tools to determine

the desired volume (V in [l]) and power (P in [kW]) of a hot water charging system

(ISSO-55, 2001). The resulting dimensions of the heating systems based on SIMDEUM

are compared with dimensions proposed by different suppliers of heating systems,

presented in Table 1. To know which dimensions are required to meet the comfort in a

building, the measured hot water demands, when available, are also applied in the same

design tool for a heating system. The dimensions based on measured hot water demands

are also given in Table 1. For a small business hotel, the actual measured hot water

demand, during full occupation requires a heating system with a volume of 500 litres

and a power of 30 kW. The dimensions resulting from the by SIMDEUM predicted hot

water demand are 500 litres and 35 kW. The supplier, on the other hand, proposes for

this hotel a heating system of 1000 litres and 200 kW. This comparison shows that

SIMDEUM does not underestimate the dimensions of the heating system, while the

supplier’s overestimation is very large. This tendency is generally found for different

types of buildings (Table 1), showing that SIMDEUM based design rules result in

heating systems that still fulfil the desired comfort wish, while being more energy

efficient.

Figure 1 Comparing average measured and simulated demand of cold (a) and hot (b)

water of an apartment building

0 6 12 18 240

0.5

1

1.5

2

2.5

time [h]

flow

(m

3/h

)

(a) measured

simulated

0 6 12 18 240

0.5

1

1.5

2

2.5

time [h]

hot flow

(m

3/h

)

(b) measured

simulated


27

Table 1 Dimensions of heating systems for different type of buildings, based on

measured hot water demand, based on SIMDEUM and proposed by companies

type of building

design based on

measurements

design based on

SIMDEUM

proposal

company

V [l] P [kW] V [l] P [kW] V [l] P [kW]

apartment building I: standard apartments 500 40 500 60 500 110

apartment building II: luxurious apartments 500 55 500 82 1000 80

hotel I (small business)a 500 30 500 35 1000 200

hotel II (large business) 1000 85 1000 60 4000 200

hotel III (tourist) 250 50 740 100

nursing home I: care needed residents 250 30 500 45

nursing home II: self-contained apartments

with independent resident

500 25 1000 100

ad a: based on measured cold and hot water demand during full occupation.

2.3 Discussion

The reliable prediction of cold and hot water demand by SIMDEUM and the

SIMDEUM based design rules yield a significant contribution in the energy efficient

design of hot water installations. Especially in non-residential buildings the suppliers of

heating systems propose heaters with too large capacities, both in volume and power

that do not match with the actual hot water demand. The proposed capacities are 2 and

sometimes 4 times larger than needed. Thus, the improved knowledge from the

SIMDEUM based design rules will lead to a more energy efficient choice of the hot

water systems. An enormous energy-saving is gained here. Moreover, the smaller

design of the heating system reduces the stagnancy of water, leading to less hygienic

problems.

3 Case II: SIMDEUM® in design of on-site/decentralised grey water

recycling and rainwater harvesting systems

3.1 Introduction

In urban areas, provision of water resources and treatment and disposal of wastewater is

a major concern. In the transition towards more sustainable urban water systems,

increasing attention is given to self-sufficiency (Rygaard et al., 2011). On-site systems

for wastewater recycling and rainwater harvesting are options to locally supply water

resources for non-potable demand. However, there are no specific guidelines for design

of these systems due to the lack of detailed information about temporal variations of

water demand at building level.

When analysing residential water demand, it becomes clear that only a small percentage

of (high quality: potable) water is used for drinking and cooking. The rest is used for


non-potable purposes, mainly for personal hy

quality than water that is fit for human

supply of local resources follows a dynamic pattern fluctuating on time. Temporal

fluctuations are given by changes in daily, wee

patterns. The demand and supply patterns are influenced by the household size and the

building characteristics. These temporal variations imply storage to match supply and

demand. However, often this dimensioning

size and average hourly or

yields. When designing on-site and

variations is crucial to evaluate storage imp

and operation (Agudelo-Vera, 2012)

Different variables determine the actual harvest of local resources: s

depending on building typology (e.g. single houses versus apartment blocks); seasonal

and location-bound variables

temporal variables (demand and supply patterns that fluctuate through the day

day/night, within the week –

(Figure 2). Our objective was

storage capacity.

Figure 2 Variables influencing the water cycle at building level.

3.2 Approach

3.2.1 Aggregation of patterns

We focused on supplying non

grey water1 (LGW) and rainwater harvesting

selected: a freestanding house

apartments of two-people household)

1 Wastewater from the shower and bath is referred to as light grey water (LGW). LGW is the

fraction of the residential wastewater.

28

purposes, mainly for personal hygiene and cleaning, which require

quality than water that is fit for human consumption. Moreover, residential demand and


fluctuations are given by changes in daily, weekly and seasonal demand and supply

The demand and supply patterns are influenced by the household size and the

These temporal variations imply storage to match supply and

dimensioning is based on average data (average household

hourly or daily consumption), which results in overestimati

site and decentralised systems, understanding these temporal

to evaluate storage implications and provide guidelines for design

Vera, 2012).

Different variables determine the actual harvest of local resources: spatial varia


bles (e.g. yearly rain patterns, depending on locations) and

(demand and supply patterns that fluctuate through the day

–working days/weekends, and within the year

. Our objective was to gain insight into the effect of dynamic patterns on

Variables influencing the water cycle at building level.

Aggregation of patterns

We focused on supplying non-potable demand (toilet and laundry) by recycling light

and rainwater harvesting (Figure 3). Two building types were

selected: a freestanding house (four-people household) and a mid-rise apartment flat

people household). Yearly patterns demands of non-potable water

Wastewater from the shower and bath is referred to as light grey water (LGW). LGW is the

fraction of the residential wastewater.

, which require lower

esidential demand and


kly and seasonal demand and supply

The demand and supply patterns are influenced by the household size and the

These temporal variations imply storage to match supply and

(average household

overestimation of

d systems, understanding these temporal

provide guidelines for design

patial variables


(e.g. yearly rain patterns, depending on locations) and

(demand and supply patterns that fluctuate through the day –

working days/weekends, and within the year – seasons)

gain insight into the effect of dynamic patterns on

Variables influencing the water cycle at building level.

potable demand (toilet and laundry) by recycling light

wo building types were

rise apartment flat (28

potable water

Wastewater from the shower and bath is referred to as light grey water (LGW). LGW is the cleanest


and patterns of production of

SIMDEUM. Although the yearly water demand per person is similar for both

households, they do not satisfy the superp

demand pattern of the four-people households is not two times the pattern of the two

people households. This non-

frequency of) water appliances related to

(adults/children).

Figure 3 Description of the two building units investigated and the storage and

treatment system modelled

3.2.2 Potential to harvest local resources

Residential water flows can vary significantly from day to day. Furthermore, daily

water demand is un-evenly distributed during the day.

rainwater harvesting are neither simultaneous nor equal in quantity with actual demands

for toilet flushing and laundry machine.

harvested – the actual harvest

given by the storage capacity of the subsystem.

the measures and to estimate the storage capacity needed, it is important to investigate

also the variations of the daily pattern.

The water balance for the building unit was evaluated for different variables such as

tank size, treatment capacity, household

assumed to treat the LGW. Thus, hydraulic residence time

treatment capacity – k – define the volume of the treatment unit

off a roof can be estimated based on the lo

Aroof [m2] and the runoff coefficient

value that estimates the portion of rainfall that becomes runoff, taking into account

losses due to spillage, leakage, catchment surface wetting and evaporation. Typical

runoff coefficient values range between 0.

harvesting potential of rainwater was evaluated for

2010 (811mm).

0 6

De

ma

nd

29

and patterns of production of LGW at hourly time step were simulated using

Although the yearly water demand per person is similar for both

households, they do not satisfy the superposition principle, meaning that the water

people households is not two times the pattern of the two

-linearity is, among others, caused by differences in (use

frequency of) water appliances related to household size and family composition

Description of the two building units investigated and the storage and

treatment system modelled.

Potential to harvest local resources and storage needs


evenly distributed during the day. Production of LGW and


et flushing and laundry machine. Only a percentage of the potential can be

the actual harvest – because of daily water demand patterns and restrictions

given by the storage capacity of the subsystem. Therefore, to evaluate the efficiency of

measures and to estimate the storage capacity needed, it is important to investigate

daily pattern.

he water balance for the building unit was evaluated for different variables such as

tank size, treatment capacity, household size and roof area. A plug-flow reactor was

assumed to treat the LGW. Thus, hydraulic residence time – RT – and volumetric

define the volume of the treatment unit. Harvesting of rainwater

a roof can be estimated based on the local precipitation – P [mm y-1

], the roof area

the runoff coefficient – RC [-]. The runoff coefficient is a dimensionless



runoff coefficient values range between 0.7 and 0.9 (Farreny et al., 2011).

harvesting potential of rainwater was evaluated for using the rainfall records of the year

12 18 24Time

0 6 12 18 24

De

ma

nd

Time(hr) (hr)

at hourly time step were simulated using

Although the yearly water demand per person is similar for both

osition principle, meaning that the water

people households is not two times the pattern of the two-

linearity is, among others, caused by differences in (use

household size and family composition

Description of the two building units investigated and the storage and


roduction of LGW and


nly a percentage of the potential can be

because of daily water demand patterns and restrictions

Therefore, to evaluate the efficiency of

measures and to estimate the storage capacity needed, it is important to investigate

he water balance for the building unit was evaluated for different variables such as

flow reactor was

and volumetric

arvesting of rainwater

], the roof area –

. The runoff coefficient is a dimensionless



2011). The

the rainfall records of the year


30

3.3 Results and discussion

A proper choice of the storage capacities results in optimisation of local harvest of

resources and in minimisation of the overflows. Overflows minimisation will reduce the

wastewater production. Selecting the optimal storage capacity involves trade-offs,

because it depends on space availability and cost. Moreover, if the storage capacity is

small, it will be most of the time full being volumetric effective, but leaving easily

excess to overflow. Figure 4 shows that actual recycling and harvesting is a function of

the building type (occupancy), storage capacity, and treatment capacity for recycling.

Notice that similar on-site systems configuration will perform different according to

occupancy.

In Figure 4, three scenarios are plotted: i) recycling, ii) rainwater harvesting and iii)

combining recycling and rainwater harvesting. For the scenarios including recycling,

two storage units and a treatment unit are required. For rainwater harvesting, a single

tank is considered. A comparison between recycling and multi-sourcing shows that for

the same storage capacity, recycling is more beneficial. If recycling and multi-sourcing

are combined, the maximum yield is achieved with a smaller storage capacity.

Comparing the two building units, for a storage capacity of two tanks of 50 litres per

person, the yield of recycled water is 39 m3/year = 10 m

3/ person year for the free-

standing house, meanwhile the same storage capacity will yield 709 m3/year = 12.7

m3/person per year.

Figure 4 Comparison of recycling and rainwater harvesting at building level

0

10

20

30

40

50

60

70

0 200 400 600

Loca

l w

ate

r re

sou

rce

pro

du

ctio

n (

m³

y-1

)

Storage capacity of each tank (l)

Free standing house - 4 people

Rainwater harvesting + LGW recyclingDQ2

LGW recycling

Rainwater harvesting

Non-potable demand = 65 m³ y-1 = 16 m³ y-1 p-1

Potential recycling = 85 m³ y-1 = 21 m³ y-1 p-1

Potential rainwater harvesting = 48 m³ y-1 = 12 m³ y-1 p-1

Treatment rate = 160 l d-1 = 40 l d-1 p-1

10 m³ p-1 y-1

0

200

400

600

800

1000

1200

0 2 4 6 8

Loca

l w

ate

r re

sou

rce

pro

du

ctio

n (

m³

y-1

)

Storage capacity of each tank (m³)

Mid-rise flat - 56 people

Rainwater harvesting

LGW recycling

Rainwater harvesting + LGW recycling

k= 2240 l d-1k= 2240 l d-1

Non-potable demand = 1108 m³ y-1

Potential recycling= 930 m³ y-1

Potential rainwater harvesting= 512 m³ y-1

Treatment rate = 48 m³ y-1

DQ2

Non-potable demand = 1108 m³ y-1 = 20 m³ y-1 p-1

Potential recycling = 930 m³ y-1 = 17 m³ y-1 p-1

Potential rainwater harvesting = 512 m³ y-1 = 9 m³ y-1 p-1

Treatment rate = 2240 l d-1 = 40 l d-1 p-1

14 m³ p-1 y-112.5


31

Overall, our results show that there are two types of constraints to satisfy water demand

with local resources at the building level. The first type is related to the availability of

local resources. Constraints to meet non-potable demand are caused by disparity

between grey water production patterns and demand patterns, and to limited availability

of rain water related to local context (i.e. climate, roof areas). The second type follows

from the first and is caused by practical limitations in harvesting the available resources.

In this case, the harvest of available resources are constrained by the storage capacities

that are required to cater for the mismatch in water harvested and demand patterns,

which is linked to the availability of space in the building unit. Results of the modelling

study showed that dimensioning of the storage capacity requires considering treatment

requirements, daily water supply-demand patterns and the presence of saving devices, in

addition to the physical space available.

This study showed that different building types, displayed different demands and

different temporal patterns associated with different occupancies and building

characteristics. This is essential information to design and optimise on-site recycling

and multi-sourcing measures. Variations in daily production and demand patterns

showed large effects on the efficiency of the resources harvested.

SIMDEUM helps understanding of process dynamics relevant for water resources

management in the built environment. We have studied the urban water balance at

building level and evaluated implementation of various measures: demand

minimisation, recycling of light grey water and harvesting of rainwater to supply non-

potable demand. SIMDEUM also allows simulation of blocks or neighbourhoods.

Simulating residential patterns using SIMDEUM can be used by urban (water)

managers and decision makers to better understand the urban water system. Better

understanding of urban flows will allow the design of customised solutions for existing

and new buildings, because an optimal scale of management of certain flows can be

identified. In the future, this type of information can support the implementation of real

time control measures to softened peak demands and to achieve smart water grids.

4 Case III: SIMDEUM® in recovery of thermal energy and resources

from wastewater

4.1 Introduction

Residential wastewater contains thermal energy and nutrients. These can potentially be

harvested. The harvesting process will be more efficient with a good understanding of

the quantity and the location and time of the various discharge flows.

The temperature of the discharged water is raised when households heat their drinking

water for bathing and cleaning or when the water in the drinking water installation has

ample time to approach the room temperature. Especially in the winter when homes are

heated and the drinking water enters the home at a relatively low temperature (10 °C,

Blokker and Pieterse-Quirijns, 2012) this could be a relevant aspect. Water is used in

toilets to discharge urine and faeces. Bathing water and washing water contain soap

residues, especially the first rinse. It is possible to quantify when and how much


32

nutrients and thermal energy are being discharged with the residential wastewater with

detailed results from SIMDEUM.

4.2 Approach

There are several steps that need to be taken to quantify the energy in the wastewater.

4.2.1 Step 1: discharge intensity and duration

The first step is to adapt SIMDEUM from a demand model to a discharge model. This

means adapting the intensity (L/s) and duration (s) of the various end uses to realistic

values that describe the discharge of water. The basis is described in Blokker et al.

(2010). The discharges that are equal to the demand are for the end uses at the bathroom

tap, kitchen tap (except for the sub end use “doing dishes”) and shower. The discharge

from WC, bath, washing machine, dishwasher and water for manual dish washing are

different; the outside tap does not discharge to the residential sewer. Table 2 shows the

values for the Netherlands. The duration follows from the demand volumes (intensity

multiplied by duration in Blokker et al. (2010)) divided by the new discharge values.

Table 2 Duration and intensity of water discharge for several types and sub types

of end uses in the Netherlands, average (µµµµ) and probability distribution function

(pdf) (NEN3215, 2011; De Paepe et al., 2003; Persson, 2007).

End-use type / subtype Duration Intensity (L/s)

µ µ µ µ pdf µ µ µ µ pdf

Bathtub 120 litres 2 min N.A. (fixed) 1.0 N.A. (fixed)

Bathroom tap Washing and

shaving

40 s Log-

normal

0.042 Uniform

Brushing teeth 15 s

Dish washer Brand and type Specific dishwashing pattern (3 cycles of water discharged, total 19

seconds, 0.75 L/sec = 14 L)

Kitchen tap Consumption 16 s Log-

normal

0.083 Uniform

Doing dishes 6 s 1.000

Washing hands 15 s 0.083

Other 37 s 0.083

Outside tap Garden N.A. Water is not discharged to sewer

Other

Shower Normal 8.5 min χ2 0.142 N.A. (fixed)

Water saving

type

0.123

Washing

machine

Brand and type Specific washing pattern (3 cycles of water discharged, total 67

seconds, 0.75 L/sec = 50 L)

WC 6-litre cistern 3 s N.A. (fixed) 2 N.A. (fixed)

9-litre cistern 9 s

The frequency of discharge is equal to the frequency of the demand (Blokker et al.,

2010). The time of discharge is not always equal to the time of the demand. The bath

tub can be emptied 10 minutes to 1 hour after it is being filled. The intake and discharge

of washing machine, dishwasher and emptying the sink after doing the dishes also

shows a shift in time. The other end uses are typically instantaneously being discharged

into the sewer. For the washing machine and dishwasher a supplier has provided us with

both intake and discharge patterns. The discharge patterns can thus be used. For the


33

time lag between filling and emptying the bath and kitchen sink there is no information

available. Because of the lack of information and the fact that the intake times are

already determined through a Monte Carlo simulation, there is no specific time lag

being introduced in SIMDEUM for discharge patterns.

4.2.2 Step 2: temperature of discharged water

The second step is to add information on temperature of the discharged water. The

bathtub is filled with water at 40 °C, and presumably discharged at 35 °C. The water for

showering is 38 °C from the shower head and we measured a temperature decrease of 3

°C from shower head to drain. The washing machine in the Netherlands typically has a

programme at 40 °C and 60 °C. This means that the first intake is heated towards the set

temperature once. We measured that the temperature of the discharged water of the first

release was 35 °C and 52 °C respectively. The water of the second and third release (see

also Table 2) has the temperature of the cold water intake. The same is assumed for the

dishwasher. The temperature of the discharged water for washing and shaving at the

bathroom tap is assumed to be 35 °C, similar to the bath and shower water. The

temperature of the discharged water for doing the dishes at the kitchen tap is assumed to

be 45 °C, as the intake is assumed to be 55 °C (Foekema and Van Thiel, 2011). The

temperature of discharged cold water (at bathroom tap, kitchen tap and toilet) is

assumed to be 10 °C at all times. This could be varied depending on the season and

residence time in the drinking water installation. The final temperature of the total

discharge volume leaving a building is calculated by mixing the discharged volumes of

the appliances with the corresponding temperature using an energy balance.

4.2.3 Step 3: nutrient load of discharged water

The third step is to add information on the nutrients in the discharged water. We first

will only consider nutrients from urine as they are discharged with a normal flush toilet.

Ca. 8.5 to 13 g nitrogen per person per day is being discharged via urine and faeces

(Kujawa-Roeleveld and Zeeman, 2006). With an average toilet visit of 6 per person per

day (Foekema and Van Thiel, 2011) it is assumed that 1.5 g nitrogen is being

discharged per toilet flush. The urine is diluted with 3 L (50% flush of a 6 L toilet

cistern) or 9 L (full flush of a 9 L toilet cistern) and a negligible amount of urine.

4.2.4 Step 4: run simulations and analyse results

The fourth step is to do the simulations and analyse the resulting discharge patterns. The

simulations are being done as described by Blokker et al. (2010). The results are a set of

possible discharge patterns. These can be further analysed on temperature and nutrient

load.

4.3 Results and discussion

The adaptations to SIMDEUM to generate discharge patterns including information on

the thermal energy and nutrient load have been identified. An example of discharge

patterns is shown in Figure 5 for a residential building, without bath. Further analysis of

the patterns renders valuable information for recovery purposes. They will also serve to

have a more accurate design of the grey water and rain harvesting systems, and to

estimate a more realistic peak reduction (in drinking water distribution) and (wastewater

discharge) due to local resources.


34

As residential sewers are unpressurised systems there is a delay between the discharge

at the home and the intake at the wastewater treatment plant. In the sewers there is an

exchange of thermal energy between the water in the sewer (from all the various

sources) and the temperature of the surrounding soil and outside air. Also, there is

dilution of the nutrient load. This should be further analysed with the help of a hydraulic

model of the sewer system filled with the specific discharge patterns from the new

SIMDEUM approach. The extra functionality of the hydraulic sewer model that will

allow for the water quality analysis needs to be developed.

5 Conclusion

SIMDEUM® can simulate residential and non-residential cold and hot water demand

patterns as well as characteristics of a building’s discharge, like discharge flow,

temperature and concentration of nutrients. In this paper three successful applications of

SIMDEUM for sustainability in water supply and drainage are illustrated. First,

SIMDEUM based design rules reduce the design of a heater capacity with a factor 2 to 4

compared to suppliers proposals, while still meeting the desired need and comfort.

Second, SIMDEUM assists in a proper choice of storage capacities in grey water

recycling and rainwater harvesting systems. It prevents the storage tanks to be

overdimensioned and can be used in continuous simulations of recycle systems. Third,

Figure 5 Demand and discharge characteristics of a residential building on 5 minute

time base.

0

10

20

30

40

tem

pera

ture

[°C

]

Tdischarge

0:00 6:00 12:00 18:00 24:000

0.05

0.1

0.15

0.2

nutr

ien

ts [

g/l

]

discharge nutrient load

0

200

400

600

800

Q [

l/h]

total water demand

hot water demand

0

200

400

600

800

Q [

l/h]

total discharge

0

200

400

600

800

Q [

l/h

]

shower demand

shower hot water

0:00 6:00 12:00 18:00 24:000

25

50

75

100

Q [

l/h

]

WC water demand


35

SIMDEUM’s information on discharge characteristics can be used to study possibilities

to recover energy and nutrients from wastewater. They will also serve to have a more

accurate design of the grey water and rain harvesting systems.

Due to its physical basis, SIMDEUM can be used for other countries, buildings and

purposes, when specific information on users and appliances is available. Influences of

future developments, like behavioural changes (more conscious, or more luxurious),

demographic changes (aging), technical progress (other appliances), legislative control,

climate changes, can be easily investigated with scenario studies based on SIMDEUM.

6 References Agudelo-Vera C. M., ‘Dynamic water resource management for achieving self-sufficiency of

cities of tomorrow’, PhD thesis, Wageningen University, 2012.

Blokker E.J.M. and Pieterse-Quirijns E.J., “Modelling temperature in the Drinking Water

Distribution System”, Journal of Water Resources Planning and Management, submitted for

publication, 2012.

Blokker E.J.M., Pieterse-Quirijns E.J., Vreeburg J.H.G. and Van Dijk J.C., “Simulating

Nonresidential Water Demand with a Stochastic End-Use Model”, Journal of Water Resources

Planning and Management, Volume 137, Number 6, p. 511-520, 2011.

Blokker E.J.M., Vreeburg J.H.G. and Van Dijk J.C., “Simulating residential water demand with

a stochastic end-use model”, Journal of Water Resources Planning and Management, Volume

136, Number 1, p. 19-26, 2010.

De Paepe M., Theuns E., Lenaers S. and Van Loon J., “Heat recovery system for dishwashers”,

Applied Thermal Engineering, Volume 23, Number 6, p. 743-756, 2003.

Farreny R., Morales-Pinzón T., Guisasola A., Tayà C., Rieradevall J. and Gabarrell X., “Roof

selection for rainwater harvesting: Quantity and quality assessments in Spain”, Water Research,

Volume 45, Number 10, 2011.

Foekema H. and Van Thiel L., ‘Watergebruik thuis 2010’ Technical report C7455. TNS NIPO,

in opdracht van Vewin, 2011

Frijns J., “Towards a common carbon footprint assessment methodology for the water sector”,

Water and Environmental Journal, Volume 26, p. 63-69, 2012.

Frijns J., Middleton R., Uijterlinde C. and Wheale G., “Energy efficiency in the European water

industry: learning from best practices”, Journal of Water and Climate Change, Volume 3,

Number 1, p. 11-17, 2012.

ISSO-55 ‘ISSO - publicatie 55; Tapwaterinstallaties voor woon- en utiliteitsgebouwen’

Stichting ISSO, Rotterdam, 2001.

Kujawa-Roeleveld K. and Zeeman G., “Anaerobic treatment in decentralised and source-

separation-based sanitation concepts”, Reviews in Environmental Science and Bio/Technology,

Volume 5, Number 5, p.115–139, 2006.

Nederlands Normalisatie-instituut, ‘NEN3215:2011 Drainage system inside and outside

buildings – Determination methods for drainage capacity, water and air density and distance for

roof mounted outlets’, 2011 In Dutch.

Persson T., “Dishwasher and washing machine heated by a hot water circulation loop”, Applied

Thermal Engineering, Volume 27, Number 1, p. 120-128, 2007.

Pieterse-Quirijns E.J., Beverloo H. and Van der Schee W. ‘Validation of design rules for peak

demand values and hot water use in non-residential buildings’, Water Supply and Drainage for

Buildings CIBW62 symposium Aveiro, Portugal, 2011.

Pieterse-Quirijns E.J. and Van der Schee W., ‘Development of design rules for peak demand

values and hot water use in non-residential buildings’, Water Supply and Drainage for Buildings

CIBW62 symposium, Sydney, 2010.


36

Rygaard M., Binning P.J. and Albrechtsen H.J., “Increasing urban water self-sufficiency: New

era, new challenges”, Journal of Environmental Management, Volume 92, Number 1, 2011.

Van Leeuwen C.J., Frijns J., Van Wezel A. and Van de Ven F.H.M., “City blueprints: 24

indicators to assess the sustainability of the urban water cycle”, Water Resources Management,

Volume 23, Number 4, 2009.

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domestic "used water"”, Bioresource Technology, Volume 100, Number 23, p. 5537-5545.

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7 Presentation of Author(s)

Dr. E.J. Pieterse-Quirijns MSc. KWR Watercycle Research Institute.

P.O. Box 1072. 3430 BB. Nieuwegein. the Netherlands; +31 (0)30 6069

672; fax +31 (0)30 6061 165; email: ilse.pieterse @kwrwater.nl

Ilse Pieterse is scientific researcher at KWR in the area of drinking water

distribution. Her main experience is the application and development of

models in a wide range of fields: water demand, temperature in the

distribution network, valve reliability.

For further information see www.kwrwater.nl

Dr Claudia Agudelo-Vera is a Researcher in the Sub-department of

Environmental Technology at Wageningen University. Her research

interests include the urban resources management, urban planning and

technology implementation towards more sustainable urban

environments. Her research focuses on understanding resources flows in

cities using different temporal and spatial scales.

Dr. E.J.M. Blokker MSc. KWR Watercycle Research Institute. P.O. Box

1072. 3430 BB. Nieuwegein. the Netherlands; +31 (0)30 6069 533; fax

+31 (0)30 6061 165; email: mirjam.blokker @kwrwater.nl

Mirjam Blokker is scientific researcher at KWR in the area of drinking

water distribution. Her speciality field is developing models to simulate

the water demand in drinking water networks.

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