Measurement and thermal characterization of graywater discharge events for house-central collection systems in the context of heat recovery Open Access

Due to increasing demands on the efficiency of domestic energy systems, there is a growing interest in the thermal utilization of wastewater and its sub-streams (Frijns et al. 2013; Elías-Maxil et al. 2014; Hao et al. 2019). There are already practically proven systems on the level of individual tabs, houses, or neighborhoods (Hepbasli et al. 2014; Sun et al. 2014). While only heat pump systems are used at the neighborhood level due to the low temperatures of mixed wastewater, there is the possibility of thermal heat recovery within the building using existing temperature gradients, which can reduce system complexity and energy consumption (Ni et al. 2012; McNabola & Shields 2013; Nolde 2013; Deng et al. 2016). In this case, a separate collection of gray and black water is essential to avoid the cooling of warm wastewater by mixing. A further benefit is the reduced load of organics and particulate matter and thus the easier utilization of separated graywater in terms of process engineering. In this field, system concepts have already been developed to market maturity at the tap and house level. Further targeted separation of hot and cold graywater streams could further increase the effective temperature gradients (Meggers & Leibundgut 2011; Nolde 2016). One fundamental problem of passive domestic central heat recovery systems is the temperature mixing in collection tanks, which prevents a more efficient countercurrent design and reduces achievable preheating temperatures. Nolde (2016), therefore, recommends the use of plate heat exchangers with low thermal inertia and combination to thermally stratified storage tanks, the dimensioning of which would require a database on the temperature dynamics of domestic graywater systems.

In parallel, the use of active system combinations (use of heat pump) is continuously studied and developed in an increasing number of publications (Hervás-Blasco et al. 2020; Hadengue et al. 2022). By Hadengue et al. (2022), an average electrical energy saving of 1.8 kWh/week was calculated for the combination of an air-source heat pump and active graywater heat recovery. For a closed-loop configuration for the sole supply of hot water, the coefficient of performance could even be improved by about 20% over the course of the year. There is also the possibility of integrating upstream passive heat recovery, which could further increase recovery efficiencies. However, no literature could be found on this topic. The probability of individual water uses over the course of the day was investigated by Sitzenfrei et al. (2017) and Wärff et al. (2020). Based on these findings, a statistical model was developed by these authors to calculate wastewater volume flows and temperatures in larger collecting systems.

Input for these calculations includes estimations on the mean temperature of different types of graywater and their statistical distribution. In Bertrand et al. (2017b), a compilation of tab-specific generation types of graywater, including their usual end-of-use temperatures, frequencies, and durations, is provided. The work states a lack of valid temperature measurement data. The data used originates from Yao & Steemers (2005) and Recknagel & Schramek (2007), which, however, only contain planning figures for hot water systems and therefore do not represent a valid measurement. As a source of values in Yao & Steemers (2005), a PhD thesis is cited, which could not be accessed (Marsch 1996). By Wong et al. (2010), heat losses within shower (SH) cabins were investigated and a correlation for heat losses based on their temperature was introduced. Here, an average drain temperature of 40.9 °C was measured but the drainage in the house system was not included. In Wärff et al. (2020), temperature values are derived from Sitzenfrei et al. (2017). However, in Sitzenfrei et al. (2017), a Bachelor thesis, which could not be accessed, is cited as the source of these values (Hillebrand 2014). Similarly, in Frijns et al. (2013), a research report, which also could not be accessed, is cited for the mentioned wastewater temperatures (SenterNovem 2006).

Some studies have already been carried out measuring the volumes and temperatures of graywater at a building level (Nolde 2013, 2016; Sievers et al. 2018). The median inhabitants (inhab)-specific daily volume flow of graywater is 67 L/(inhab*d) with a range of 19–108 L/(inhab*d) (Sievers et al. 2018). The temperatures measured showed a range of 10–50 °C with median values of 20–30 °C (Masi et al. 2007; Hernández Leal et al. 2010; Sievers et al. 2018). However, the data mentioned in the existing system are often conducted as supplementary measurements, so a detailed description of the measuring method is lacking, and measurement intervals are in the range of hours or days. No work could be found containing temperature monitoring data of graywater systems with high temporal resolution.

In view of the development of passive or active heat recovery within medium to larger buildings or smaller quarters, it is still unclear which temperature profiles of graywater can be expected depending on the number of occupants, the taps connected, and the piping system. Especially for thermal uses that are limited in their heat consumption (e.g. hot water preheating), the question arises of which taps should be optimally connected for specific use in order to create maximum graywater temperatures. However, for a realistic analysis, it is essential to represent the heat losses as well as the flow behavior in domestic drainage systems as accurately as practically possible. Despite the multitude of literature, there is no detailed analysis of heat losses of wastewater and graywater within domestic drainage systems.

Following this, there is also no research on the design implications of domestic graywater drain systems in the context of heat recovery. Especially for systems on the building level, this results in a large planning uncertainty. Within this work, the thermal behavior of graywater within domestic drainage systems is described in detail on the basis of a measurement campaign within a single-family house with separate graywater collection and discharge. The aim of this work is to provide a database for dynamics of temperatures and volume flows of individual discharge events within a single-family house with separate graywater collection and discharge in gravity flow. This includes especially a differentiated recording of the temperatures present in the graywater after discharge in the building and their temporal behavior. This work thus provides a novel database, which on the one hand can make existing simulation work more precise and on the other allows new simulative considerations. In particular, the time scale of control loops within systems for graywater heat recovery can be considered in a differentiated way for the first time, and more efficient system solutions can be developed.

The measurements were carried out in the period from 01 October 2021 to 02 November 2021 within a single-family house with two residents in Weimar, Germany. Within the two-story building, there are two bathrooms, each with a handwash basin (HW) and SH, a bathtub (BT) in the upper floor bathroom, and a washing machine (WM) in the lower floor bathroom. Furthermore, within the kitchen, there is a kitchen sink (KS) as well as a dishwasher (DW). The graywater is collected via two DN 60 PET pipes systems, which are both vented through the roof. In the design of the piping system, a separate collection of bathroom and kitchen graywater was provided. In the basement of the building, these are joined together, whereby a separate collection is possible via valves.

All measurements are shown in the Supplementary material. Parallel to the measurement, user behavior was recorded using questionnaires, on which the users noted on which time and date an individual tab was used. The results of the questionnaires are also shown in the Supplementary material. For DWs (Bosch) and WMs (AEG), electricity consumption (Voltcraft Energy Logger 4000) was also measured on a long-term monitoring basis in order to ensure that the time of individual operating and emptying cycles was reliably recorded. The results are included in the Supplementary material. Discharge events could be identified by the comparably small current demand of the pump of each device.

By means of a function, a vector was generated, which contains a check for a discharge event based on a limiting volume flow of 0.5 L/min. Using this vector, subsets of individual contiguous discharge events were stored within a list. For a complete mapping of the events, a period of 15 s for and after a change of state of the above vector was included. For each subset, a statistical evaluation of the event was performed and stored within a data frame.

A consistent error analysis of the determined measured values could only be found in Sievers et al. (2018). However, due to the data acquisition via impulse output, no distinction can be made between zero values and measurement errors. In Alnahhal & Spremberg (2016), for example, temperatures were measured outside the drainage pipe and volume flows were only recorded on the drinking water side. Due to the small number of measurements published in peer-reviewed journals, some gray literature was used as a comparison. However, this literature lacks methodological descriptions of the measurement techniques used (Nolde 2016, 2013). In conclusion, it must be emphasized that volumetric flow measurement in untreated wastewater is susceptible to measurement errors and there is a need for differentiated measurement value processing and discussion with the aid of metadata.

In this evaluation, an under-sampling of larger discharge events (especially SH and BT) is to be assumed, caused by the increasing proportions of measurement errors along with the discharge volumes. These figures refer to the quantifiable discharge events and thus allow a valid statement to be made on the flow rate and temperature curves of various tabs, but not on their frequencies. Furthermore, DW and WM, depending on the type of device and program, show coherent peak series for each use, which are included here as individual events. For this reason and the relatively low number of users, a comparison with existing literature data on usage frequencies (Bertrand et al. 2017a, b; Sitzenfrei et al. 2017; Wärff et al. 2020) is not suitable. However, there appears to be an overall lower frequency of use of these devices.

Compared with the statistics on wastewater generation used by Wärff et al. (2020), the total duration of some discharge events and thus also the volumes were determined to be lower. For SHs, for example, mean durations of 576 s with 12 L/min and thus a volume of 115 L were applied in this source. Thus, a more economical usage behavior (lower SH duration) as well as a more economical SH head (6–8 L/min) was found in this study. The determined duration is in greater agreement with other authors (Bertrand et al. 2017b). However, the determined BT data are nearly identical to those in Wärff et al. (2020) (duration 444 s; flow rate 10.5 L/min). A comparison of the WM and DW data is not possible due to more variable representations (individual peaks vs. total volume per cycle). Basically, the determined values seem to be within the range of variation possible for single-family buildings.

Compared with the measured values in Nolde (2013), similar progressions of the cumulative curve can be seen. Here, the median is 32 °C with a temperature range of 24–43 °C. A multi-family building was investigated where only the graywater from the BT and SHs of 56 residential units was connected to the separated system. Furthermore, a fluctuation of the median of approximately 2 °C in the course of the year was determined here. In the winter months, the proportion of colder temperatures was higher, while higher temperatures were present in equal proportions. The exact reason for this fluctuation (lower cold water temperature, heat losses in the piping system or exhaust air) cannot be defined on the basis of these investigations. In Knerr et al. (2009) with a range of 15–43 °C and in Menger-Krug et al. (2010) with a mean value of 30 °C an identical fluctuation range of the graywater temperatures are also determined. More significant differences exist in simulation input data. In Hadengue et al. (2022), the following mean values were assumed: SH: 36 °C, KS: 35 °C, HW: 35 °C, WM: 37 °C. Especially the temperatures of smaller tabs are strongly overestimated. The same can be seen for the drain temperature assumptions in Hervás-Blasco et al. (2020) (SH: 33 °C, BT: 33 °C, HW: 31 °C, WM: 30 °C, KS: 38 °C, DW: 46 °C) and other authors (Bertrand et al. 2017a; Wärff et al. 2020).

Regarding the causes of the temperature profiles of graywater two basic processes can be distinguished (if a mixture of different discharge events is neglected):

• Individual discharge events show variable discharge temperatures due to user- and device-specific influences (especially HW, KS, DW, and WM).

• Instationary temperature losses cause an initial rise of graywater temperatures toward a quasi-stationary plateau (especially SH, and BT).

In practice, there is an overlapping of both processes within all discharge events. In addition, the observed behavior is practically influenced by the thermal inertia of the respective temperature probe and the hydraulic volume of any siphons it is placed in. Within these measurements, these effects were reduced as far as possible.

Within this work, a detailed measurement of flow rate and temperature of a building-central graywater system was carried out. Individual discharge events could be identified and assigned to specific types of use. Due to the consistent recording of measurement errors, the accuracy of the determined data could be improved. Thus, accurate statements on the existing temperature distribution for the graywater of specific tabs could be determined. Furthermore, evaluating the time-related rate of change in temperatures could provide a novel data basis. This enables more precise simulations to be carried out. For some tabs, significant differences to previous assumptions became apparent. The high temporal resolution of the data also enables simulative considerations that go beyond previous longer-scale energy balances. Especially the separation of graywater based on its temperature prior to heat recovery should be investigated in more detail using this data.

The validity of this data is limited by the small statistical coverage due to the necessary restriction to one residential object in Germany. A transfer to multi-family buildings, neighborhood solutions and cultures with significantly different water use is not applicable. Further limitations arise from experienced measurement errors of graywater flow rates, which require careful consideration of their validity.

In conclusion, a subsequent extension of the data basis is recommended. Here, a more specific investigation of the influence of the respective object and the construction of its drainage systems should be carried out. In view of possible efficiency increases, the investigation of heat losses in the piping system should be a core aspect. In addition, there is the question of the optimum linkage size, with increasing numbers of connections also leading to increasing exergy losses due to dilution and energy losses due to longer pipes. On the other hand, specific investment costs and maintenance costs are decreasing. This question requires combined investigations at the neighborhood and building level. Therefore, more detailed measurements in multi-family buildings are recommended. For this, an automated identification of individual tabs seems necessary assuming an increasing error rate of inhabitant questionnaires and simultaneous discharge events.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.