In the urban water cycle there are different sources for extracting energy. In addition to potential and chemical energy in the wastewater, thermal energy can also be recovered. Heat can be recovered from the wastewater with heat exchangers that are located decentralized and/or centralized at several locations throughout the system. It can be recovered directly at the source (e.g. in the showers and bathrooms), at building block level (e.g. warm water tanks collecting all grey water), in sewers or at the wastewater treatment plant. However, an uncoordinated installation of systems on such different levels can lead to competing technologies. To investigate these interactions, a modelling environment is set up, tested and calibrated based on continuous sewer temperature and flow measurements. With that approach different heat recovery scenarios on a household level (decentralized) and of in-sewer heat recovery (centralized) are investigated. A maximum performance drop of 40% for a centralized energy recovery system was estimated when all bathrooms are equipped with decentralized recovery systems. Therefore, the proposed modelling approach is suitable for testing different future conditions and to identify robust strategies for heat recovery systems from wastewater.

INTRODUCTION

In the EU27, the gross inland energy consumption is approximately 40 MWh per capita per year at present (Azhar Khan et al. 2014) and approximately 20% of that is final household consumption. Within the urban water cycle, different processes consume a considerable amount of energy. Energy is consumed for exploitation, water preparation, transportation, heating, collecting, and water treatment. Approximately 80% of the primary energy demand in the (domestic) urban water cycle is required for water heating and the rest is for transportation and treatment (Elias-Maxil et al. 2014). Just in the operational phase of the urban water infrastructure, approximately 220–260 kWh per capita per year are consumed (Venkatesh & Brattebø 2011), but these values can vary quite a lot (up to 700 kWh per capita per year or even more; Olsson 2012) depending on, for example, if the system is gravity-driven or not, if a desalination plant is implemented, the level of wastewater treatment or the state of assets. For the centralized water infrastructure (without water heating), approximately 0.5–2% of the gross inland energy demand per capita is consumed and for water heating approximately 5–10%. Most of this energy amount for heating is usually discharged into the sewer system.

In principle, the water industry can produce energy due to recovery, but as long as the energy consumption per capita is that high, it can only contribute marginally to covering the required demand (Svardal & Kroiss 2011). In the urban water cycle, there are different sources for extracting energy. In addition to chemical energy in the wastewater, thermal energy can also be recovered (van der Hoek 2012). Heat can be recovered from the wastewater with heat exchangers that are decentralized and/or centralized at several locations throughout the system. Heat recovery is located directly at the source (e.g. in the showers and bathrooms), at building block level (e.g. warm water tanks collecting all grey water), at sewers or at the wastewater treatment plant (Meggers & Leibundgut 2011; Kretschmer et al. 2016). It is important to consider that the biological processes at the wastewater treatment plant are inhibited when the temperature is too low (Wanner et al. 2005). Usually, there is a difference in the soil and sewer air temperatures compared to the wastewater temperature; therefore, the temperature of the wastewater changes during the transport process to the wastewater treatment plant (Dürrenmatt & Wanner 2014). Therefore, the spatial distribution of heat sources, consumers and recovery systems is of great interest (Neugebauer et al. 2015). Even industrial cooling with wastewater could be a feasible use, but for these applications, knowledge about the temperature distribution and the impact of thermal utilization is required. Therefore, Dürrenmatt & Wanner (2014) presented a mathematical description of the effect of heat recovery in sewers. When a heat recovery system is installed in a combined sewer system, there is especially high variability in the flow rates and temperatures (Cipolla & Maglionico 2014) throughout the year, which requires extensive numerical efforts for assessment (Abdel-Aal et al. 2014). More recently, Abdel-Aal et al. (2015) showed the successful development of a predictive modelling technique to simulate wastewater temperatures in sewer pipes.

It is of great interest to understand the interactions of different heat recovery options (decentralized and centralized) to identify efficient solutions. The payback time of such systems is at least a decade (Uhrig 2016) and in such a period significant changes in warm water consumption due to technical progress and change in consumer behaviour can take place. When a centralized heat recovery system is installed in the sewer system, it can significantly be impacted by such behavioural changes and technical progress (e.g. heat recovery in showers becomes a standard). The goal of this study is to investigate the interactions of centralized and decentralized heat recovery scenarios in a wastewater system. The simulated wastewater production is based on an intensively investigated stochastic water demand model (Blokker et al. 2010) with some modifications and simplifications for this particular application. For an application to heat recovery, temperatures for the different water consumptions are implemented based on literature values. With that, a partitioning of the different heat sources in the residential heat water demand (and wastewater) over a day is possible and the impact of stepwise changes (technology diffusion) in the different sources (e.g. reduced temperature or amount) on a centralized heat recovery system can be investigated. The simulated wastewater production is compared to measured data of a residential area of approximately 10,000 inhabitants and shows sufficient results in terms of wastewater quantity and wastewater temperature over time. Based on that a modelling study of different heat recovery systems on (1) a household level (decentralized) and (2) in-sewer heat recovery (centralized) is conducted and the interactions are investigated in this work.

MATERIALS AND METHODS

A heat recovery system in sewers requires a certain amount of wastewater to operate from a technical point of view and also to operate economically reasonably. In the literature, different minimal amounts of wastewater are specified for separate and combined sewers, due to the different boundary conditions (e.g. amount and temperature of sewer infiltration water, flow velocities, air and soil temperature). Also the different biofilm build-ups on the heat exchanger (wash-off during rain events) and the intended further utilization of the heat (transport distance to consumers (e.g. for residential heating), required temperature lift in the heat pump) play significant roles (Merkblatt DWA-M114 2009). Approximately, an amount of 15 l/s dry weather flow (i.e. a population of 5,000 to 10,000) should be connected to a sewer to beneficially install a centralized heat recovery system in a sewer pipe (Klinger & Weber 2004). For a simulation of heat recovery systems, a corresponding resolution in terms of population is required. Therefore, different household end use types are used to produce wastewater hydrographs for a population of 1,000 up to 20,000 but could also be superimposed or adapted for higher numbers of population. The principal idea of this study is to investigate the interactions of centralized and decentralized heat recovery systems from wastewater. For that a wastewater production on a household level for different end use types is used as the input for sewer temperature simulations with the existing TEMPEST model (Dürrenmatt & Wanner 2014).

Wastewater production for different household types

The wastewater production model applied herein is mainly based on literature values for water demand and estimated utilization temperatures for hot water. The initial step in this investigation is to obtain the water demand at a household level with a stochastic demand model. In the literature, this has been extensively investigated (e.g. Blokker et al. 2010). Based on that work, a simplified model is applied, to obtain sufficient results for heat recovery scenarios in sewers (with ≥1,000 population connected). Based on Blokker et al. (2010), end use types as specified in Table 1 are used with the corresponding water demands (Neunteufel et al. 2014).

Table 1

Water demand data and water temperatures (Hillebrand 2014)

End use typel per usageUsage per dayμT (°C)σT (°C)μQ (l/s)σQ (l/s)μD (min)σD (min)μh,n (h) k = 1/2/3σh,n (h) k = 1/2/3
Shower 36 0.7 37 0.7 7.4 0.9 4.8 9/14/20 1.9/3/1.7 
Bathtub 76 0.03 37 0.7 10.5 0.5 7.4 0.6 10/14/20 2.4/3/2.2 
WC 5.9 6.1 8.5 0.3 0.15 9/13/20 1.9/3.5/1.7 
Washing machine 44 0.4 25 1.5 8.9 0.6 4.9 0.3 10/14/17 2/5/3 
Dishwasher 16.3 0.25 40 0.7 1.6 0.15 10/14/20 1.9/2/1.7 
Outside use 85 0.8 8.5 6.1 0.6 14 – 7/13/19 1.5/4/2.5 
Taps 1.7 21 30 2.5 0.15 0.7 0.14 9/14/20 2/2.6/1.8 
End use typel per usageUsage per dayμT (°C)σT (°C)μQ (l/s)σQ (l/s)μD (min)σD (min)μh,n (h) k = 1/2/3σh,n (h) k = 1/2/3
Shower 36 0.7 37 0.7 7.4 0.9 4.8 9/14/20 1.9/3/1.7 
Bathtub 76 0.03 37 0.7 10.5 0.5 7.4 0.6 10/14/20 2.4/3/2.2 
WC 5.9 6.1 8.5 0.3 0.15 9/13/20 1.9/3.5/1.7 
Washing machine 44 0.4 25 1.5 8.9 0.6 4.9 0.3 10/14/17 2/5/3 
Dishwasher 16.3 0.25 40 0.7 1.6 0.15 10/14/20 1.9/2/1.7 
Outside use 85 0.8 8.5 6.1 0.6 14 – 7/13/19 1.5/4/2.5 
Taps 1.7 21 30 2.5 0.15 0.7 0.14 9/14/20 2/2.6/1.8 

For each of the end use types, water temperature is assumed to be normally distributed (expected value μT and standard deviation σT see Table 1). Also water flow (expected value μQ and standard deviation σQ see Table 1) and duration (expected value μD and standard deviation σD see Table 1) of utilizations are assumed to be normally distributed. For the duration of outside utilization according to the literature a X2 distribution is assumed with an expected value μD.

This model aims to reproduce water demands/wastewater production at nodes aggregated for populations of 1,000 and more. The prediction of warm water production follows a daily variation and is thus predicted as 24 h cycles. For each of the pulses of the different end use types, the time of occurrence is estimated by means of a frequency diagram (Rauch et al. 2003). Therefore, the point in time of the occurrence of each event is estimated on the basis of a standard demand pattern (see Figure 1(b)) for that number of inhabitants (DVGW 2008). For the different end use types, a combination of three different normal distributions (k = 3) is used to obtain an empirical cumulative probability function of that frequency diagram (Equation (1)) for the time of occurrence (see Table 1 expected value μh,n) and standard deviations (Table 1 standard deviation σh,n).
formula
1
Figure 1

Case study and measurement location (a); standard demand pattern used for calibration (b).

Figure 1

Case study and measurement location (a); standard demand pattern used for calibration (b).

Based on literature values for residential water demand (DVGW 2008), the values for σQ, μQ, μh,n and σh,n are initially assumed and subsequently calibrated in order to obtain the standard demand pattern for households mentioned before. As this is a stochastic process, for calibration 10 runs are performed and the target value (= standard demand pattern) is compared with the median values of the 10 runs (see Figure 1(b)). Note that this work focuses on the interaction of residential decentralized heat recovery and centralized heat recovery; therefore, the wastewater production model is based on a standard residential demand pattern. Any commercial, industrial or communal discharges are neglected in this regard.

Temperatures from tap to sewer (in-house losses)

When recovering heat from hot wastewater, the temperatures available for recovery are not those at the consumption level. For example, when recovering heat from a shower with an instantaneous heat exchanger the wastewater temperature – as the wastewater enters the shower drain – is a few degrees lower than the consumption level due to losses. Wong et al. (2010) measured these temperature losses under different conditions for a high rise residential block and determined temperature drops between 2 and 7°C and developed an empirical equation to determine this drop. Like the shower, there are also temperature losses from the shower drain to the sewer line. As the temperatures are highest close to the source, also the losses are highest. It is assumed that this correlation can be transferred to the investigated situation. Based on Wong et al. (2010) a simplified potential equation is used in this study to determine the in-house temperature losses: ΔT = 10−10*Ti6.673 for utilization temperatures Ti (ΔT < 7.5 °C).

Temperatures along sewer lines

The wastewater flows and temperature profiles from the households are the input for the existing mathematical model TEMPEST (Dürrenmatt & Wanner 2014) to predict wastewater temperatures along sewers. TEMPEST is not intended to model the temperature characteristics in a detailed sewer model with a lot of small sewer lines, branches and loops, but to predict wastewater temperature in sewer main lines. The main line investigated in this work is shown in Figure 1(a). A simplified sewer line is used, which is supposed to drain the entire area connected to the measurement point. The parameters for underground and soil temperatures are determined based on existing measurement data for ground heat extraction with open and closed loop systems for geothermal utilizations (Sitzenfrei et al. 2011). For that region and the typical sewer depth, a soil and groundwater temperature of 12°C was found for saturated sandy soil.

Case study data

For a real application, a part of a small Alpine case study is used. In the entire drained area there are approximately 15,000 inhabitants (see marked area in Figure 1(a)). Approximately 10,000 inhabitants are connected to the measurement point. To some extent there are also commercial, industrial and communal discharges in the sewer systems. For dry weather situations, the water height and the wastewater temperature were measured. In total, 10 days of measured dry weather flow were used in this study (see Figure 3). For measurement equipment an onsite area-velocity flow meter (NIVUS PCM Pro) was installed with a monitoring interval of 1 minute. The total length of the simulated sewer line is 3 km with a diameter of 0.8 m and an average slope of 1 m/km. The average air temperature during the measurement campaign was 12°C.

RESULTS AND DISCUSSION

In the first step, the variability of the produced wastewater profiles is shown. In Figure 2 the results for 1,000, 5,000 and 10,000 persons are shown as statistical evaluations of 10 model runs. In the upper figures, for 1 day (in 6 minute time steps) the wastewater temperatures are shown, and in the lower figures, the wastewater flows. Notably, with increasing persons connected, the variability of the results decreases. While, for example, for the wastewater temperature there is variation of up to 4°C within the runs for 1,000 persons, for 10,000 persons this variation decreases to approximately 1 °C.
Figure 2

Simulated wastewater temperatures and flows for different populations.

Figure 2

Simulated wastewater temperatures and flows for different populations.

In the next step, it is evaluated how the simulated profiles agree with the measured characteristics of the investigated case study. For that the measured wastewater flows are compared with the simulated ones. In Figure 3(a), the median and the quartile values of the measured wastewater flows are shown in comparison with the simulated median values and quartiles. Whereas the characteristics of the curves agree, the variation in the measured data is higher. In this regard it has to be mentioned that the simulated data is modelled as one single discharge point in the system. In reality, the dry weather flow of the 10,000 inhabitants is piecewise discharged in the sewer system over a length of about 3 kilometres. Following this argumentation, the less distinct minimum between 12:00 and 18:00 can be explained by the spatial distribution of the inhabitants throughout the real system and the impact of commercial, industrial and communal discharges. This impreciseness of wastewater flow propagates to the temperature modelling and introduces an uncertainty of the modelled results. Nevertheless, the overall quantity of wastewater is in good agreement with the measured flows.
Figure 3

Measured and simulated wastewater temperatures and flows.

Figure 3

Measured and simulated wastewater temperatures and flows.

The measured and simulated wastewater temperatures are compared in Figure 3(b). To systematically compare the measured and the simulated wastewater temperatures, the wastewater transport and the temperature distribution along the sewer line have to be considered. For that the model TEMPEST is used with parameters according to the boundary conditions (slope, diameter, air temperature, soil conditions, assumption for sewer infiltration rate, etc.). For the simplified sewer line shown in Figure 1(a), the temperature distribution is simulated with on average 1,000 inhabitants connected to each of the 10 nodes shown as small circular markers in Figure 1. With the variation of wastewater production between 800 and 1,200 inhabitants, different spatial distributions of the population are applied by assigning different hydrographs to each of the 10 nodes representing a potential variation of the wastewater production. As a result, the wastewater temperatures for 36 hours for six of such scenarios are shown in Figure 3(b) as black lines. In comparison to that, also the measured temperatures for 10 days are shown as grey lines. From a visual comparison, also for the wastewater temperatures, the time variation could be reproduced. The bandwidth of the measured data is approximately 1°C for different days. With the modelled six scenarios for the spatial distribution of the wastewater production, almost the same amount of variation could be obtained. It has to be explicitly indicated that the input parameters for the wastewater production and wastewater temperatures on the household level are based on literature values and the data measured in the sewer line. Therefore, errors in these literature values could be compensated for by the temperature simulation models. But based on the evaluations shown above it is assumed that for the intended use the simulated wastewater flows and temperature profiles are sufficient. In the last step, the wastewater profiles are used for a modelling study to investigate the impact of different heat recovery strategies within the wastewater system.

When designing and maintaining centralized infrastructure, it is important to consider potential future decentralized infrastructure implementations (e.g. Sitzenfrei et al. 2013; Sitzenfrei & Rauch 2014). It is investigated how a decentralized heat recovery in bathrooms has an impact on a potential centralized heat recovery system. For that the average wastewater temperatures (μT in Table 1) of the shower and bathtub are assumed to be reduced from 37°C to 23°C in order to represent decentralized heat recovery systems. To investigate the development of such technology diffusion (stepwise implementation of such devices in the households), different installation rates are investigated from 0% to 100% implementation. A 40% technology diffusion rate means that 40% of the households have that technology installed. Different rates could also be interpreted as different temporal states (e.g. 0% in 2015 and 20% in 2025). In Figure 4(a) the temperature distributions for the different scenarios are shown. These temperatures are simulated close to the consumers. These modelled temperature levels would, for example, occur in heat recovery tanks for building blocks, before wastewater enters the main sewer system.
Figure 4

Results for wastewater temperatures for technology diffusion of bathroom heat recovery (IQR: interquartile range).

Figure 4

Results for wastewater temperatures for technology diffusion of bathroom heat recovery (IQR: interquartile range).

In Figure 4(b) the simulated sewer temperatures at the measurement location (big circular marker in Figure 1(a)) for the described scenario are shown. These temperatures would be the design temperatures for a centralized heat recovery system at that sewer. For an estimation of the effect of such technology diffusion, an average wastewater temperature of at least 12°C is assumed to be maintained in order to ensure a sufficient wastewater treatment at the wastewater treatment plant. With no decentralized heat recovery systems installed, a temperature difference of 3.2°C could be used for energy production while this amount can significantly decrease with increasing technology diffusion (down to 1.9°C). A sewer heat recovery system can approximately utilize 2 to 4°C. For in-sewer temperatures of 15.2°C a performance of approximately 370 kW could be achieved with such a device. With a decrease in wastewater temperature due to a 100% technology diffusion of decentralized bath heat recovery systems and the same amount of wastewater (approximately 27 l/s), this amount would decrease to approximately 220 kW, a 40% performance drop. Such a performance drop would have significant impact on the payback time of such a system.

CONCLUSIONS AND OUTLOOK

In this work, a modelling study of different heat recovery systems on a household level (decentralized) and of in-sewer heat recovery (centralized) is conducted. All of these technologies can be installed from a technical point of view and can also be operated beneficially from the point of view of energy balance but these installations on different levels can be competing with each other. The implementation of decentralized installation can hardly be controlled and is likely to be installed in, for example, bathrooms in future. A maximum performance drop of 40% for a centralized in-sewer energy recovery was estimated when all bathrooms are equipped with such decentralized recovery systems.

The payback time of centralized heat recovery systems is estimated as at least a decade. When enforcing such centralized technologies with, for example, subsidies for centralized heat recovery in sewers, it should be also taken into account that over the next 20 years temperature levels in sewer systems could change due to a progressing implementation of in-house devices. A potential solution for these competing technologies would be to enforce just one by, for example, subsidies. As private installation cannot be prohibited, subsidies just for this decentralized heat recovery in the households could be one strategy to solve this issue. On the other hand, if the transport time in the sewer system after the heat recovery is long enough, the sewer temperatures are approximately at the level of the average ground or groundwater temperatures (assuming there are no additional inputs or outputs). For an efficient planning, a further modelling study as shown in this work could identify robust strategies for wastewater heat recovery.

ACKNOWLEDGEMENTS

The research work is based on the master's thesis entitled Entwicklung eines stochastischen Abwasseranfallmodells zur Untersuchung von Wärmerückgewinnungsszenarien (Development of Stochastic Wastewater Production Model for Investigation of Heat Recovery Scenarios) by Sebastian Hillebrand, 02/2014, at the University of Innsbruck.

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