In order to better understand the current state of the industry and establish the present gaps in academic knowledge, a literature review was conducted. Specifically, publications sought for review were those focusing on WWHR systems using heat pumps, raw sewage, EnergyPlus and operating at the building level rather than at the district level. Ultimately, a great deal of insight was gained from the wide variety of publications reviewed.

Ni et al. [4] investigated a novel residential grey water energy-recovery system using heat pumps and EnergyPlus. However, the heat pump studied was an air source heat pump and EnergyPlus was used exclusively to estimate building heating and cooling loads. A numerical model was used to estimate energy impacts in various residential home settings. This study did not feature a water source heat pump fully integrated into the building model like that proposed for this project.

Hepbasli et al. [5] provided a comprehensive review of wastewater heat pump system publications from across the globe as recent as 2014. The authors reviewed 33+ publications and summarized each source's methods, analysis types (energy, exergy, economic, environmental) and main conclusions. Only one of the 33+ studies explicitly used EnergyPlus for modelling. That study was the previously mentioned residential grey water energy-recovery system authored by Ni et al. [4].

Kahraman and Alaeddin [6] used an experimental setup like the one analysed in this study. The experimental data obtained from their measurements showed a mean uncertainty value of ±2.47% for the measurement parameters. The authors reported that the heating Coefficient of Performance (COP) values were 3.36, 3.43 and 3.69 at the wastewater temperatures of 20 °C, 30 °C and 40 °C, respectively. It was also found that the maximum temperature in the energy storage tank was 50.6 °C. These insights served as good benchmarks for the computational models created for this project. It should be reiterated that while the wastewater heat pump setup was similar, no holistic building energy model was created that could be used in energy simulations.

Chao et al. [7] analysed the field performance of a WWHR-HP installed at a spa in Shenzhen, China. The authors pointed out that the volume, temperature and cleanliness of bath water captured at the building level is very different from wastewater at the district treatment plant level. The system evaluated by the author is very similar to the system that was analysed for this project, except for the type of heat pump heat exchanger and the cleanliness of the heat source water. The data collected found that higher wastewater temperatures correlated to higher COPs. Higher wastewater temperatures led to higher refrigerant temperatures at the evaporator inlet (17.9 °C, 16.6 °C, 14.8 °C) and led to COPs of 3.38, 3.01, 2.87, respectively.

Culha et al. [8] conducted a review of many wastewater heat pump/heat exchanger publications. Swiss researchers reported that more than 15% of the thermal energy supplied to buildings was lost through the sewer system. The review separated systems and equipment studied into broad classifications. According to these classifications, the WWHR-HP system investigated for this project is a "monovalent, domestic usage system with a shell-tube wastewater heat exchanger." Of the 27+ papers that were reviewed, none were categorized as a shell-tube wastewater heat exchanger used in a domestic setting and none explicitly were noted as having created integrated holistic building models using EnergyPlus.

The 2017 publication by Zhang et al. [9] investigated the performance of an installed sewage source heat pump system in China. Located within a 15,000 m² hotel, the average COP of the heat pump was found to be 6.0 and the average COP of the overall heat pump system was found to be 3.9. The heat pump was used sewer water as a heat source and sink in order to meet both building heating and cooling loads. The payback period of the system was found to be 4.7 years. Two-thirds of the power consumed by the system was used by the heat pump, while the remaining one-third was used by the system pumps. This led to the recommendation that variable frequency drive pumps should be used to adjust to varying load conditions, resulting in energy and maintenance savings. The sewage inlet temperature correlated nearly linearly and was directly proportional to the COP of the heat pump.

Spriet and Henrick [10] investigated WWHR using heat pumps and sewer integrated heat exchangers in downtown Brussels, Belgium. The heat pumps used in the study featured a COP between 3.7 and 5.0 and the sewer wastewater temperature fluctuated between 8 °C and 16 °C when measured between December and April. Monte Carlo simulations were used to predict the levelized cost of energy and total equivalent warming impact. The WWHR-HP system reduced the total equivalent warming impact by 49% compared to a natural gas water heater, but also had a greater levelized cost of energy, except for high heating demand buildings.

In the publications reviewed, none of the research involved constructing a building integrated EnergyPlus model for a WWHR-HP system. Thus, the opportunity was available to test the hypothesis that such a model could be constructed.

Because WWHR using heat pumps is relatively new and with few installed instances at the onset of this project, a deeper dive into the physical components of the WWHR-HP and overall DHW production system is provided. The Canadian manufacturer of the WWHR system that was analysed describes it as a self-contained thermal energy recovery system that is designed to produce DHW at high efficiencies [11]. The system heating capacity is available in several different sizes including 35,170 W (10 refrigeration tons, i.e., the T-10 unit), which was the unit used during lab performance testing. Table 1 displays greater detail about the T-10 unit.

The T-10 unit manufacturer contracted an independent 3^rd party to conduct performance testing on the WWHR-HP [12]. The testing involved supplying a constant wastewater temperature to the evaporator side of the heat pump and measuring the condenser side water heat transfer. The condenser and evaporator water flow rates were held constant. Five different constant wastewater temperatures were tested: 18 °C, 21 °C, 24 °C, 27 °C, 30 °C. The condenser water temperature was free to increase during testing. This water was circulated between the plate-frame condenser and a nearby tank. The heat pump COP was calculated as seen in eq. (1) [12]:

$\text{COP}=\frac{\text{Condenser water heat transfer rate}[\text{W}]}{\text{Heat pump power}[\text{W}]}$

Figure 1 displays the results from the five performance tests. As the difference between the wastewater temperature and the condenser water temperature increased, more work was required by the heat pump, which led to a decay in the heat pump COP.

Figure 1.

WWHR-HP lab test results

The WWHR-HP unit is one of five components found in a typical DHW production system. The other components are: Domestic Pre-heated Water (DPW tank) storage tanks, Wastewater storage tank (WW tank), Top-Off storage tanks (Top-Off tank) and Top-Off boilers. Figure 2 shows how a typical system might be connected.

Figure 2.

Typical setup of a WWHR-HP DHW system

Plant loops were created to best mimic the real-life behaviour of the WWHR-HP system. The formulation of this model was based upon the typical hot water production setup like that in Figure 2. In total, five plant loops were required to model the system. The plant loops were the: DHW loop, DPW loop, Top-Off loop, WW-HP loop, and WW-Water loop. The branch connection diagram for all five of these loops is seen in Figure 3.

Each plant loop can be broken into a supply and demand side. The supply side contains equipment that meets the heating or cooling load created by equipment on the demand side. The E+ Plant Manager simulates the demand half-loops simultaneously and then all the supply half-loops simultaneously and uses a series of complex integral functions and predictor/corrector equations to converge on a solution for each simulated timestep. Several of the most important governing equations are listed below. One should refer to the EnergyPlus Engineering Reference and EnergyPlus Input-Output Reference for more detailed information.

Loops

Heating or cooling demand that the supply half-loop serves (LoopDemand) is determined via eq. (2), where LoopSetPoint is the supply half-loop leaving water temperature setpoint, LoopTempIn is the supply loop entering water temperature, mdot is the water mass flow rate and c_p is the specific heat of the loop water [14] (p 467):

$\text{LoopDemand}=mdot\times c_p\times (\text{LoopSetPoint}-\text{LoopTempln)}$ (2)

Figure 3.

EnergyPlus model loop branch connection diagrams

The lab testing data provided by the T-10 unit manufacturer was used in conjunction with an EnergyPlus auxiliary program to estimate the values for coefficients C1-D5 [16]. The auxiliary program uses ordinary least squares and the Nelder-Mead optimization algorithm to determine the coefficients. These methods are based on research conducted at the University of Oklahoma State [15]. Coefficients C1-C5 and D1-D5 in eqs. (15-18) were calculated using all 1,080 observations obtained from the lab testing mentioned in the Introduction and seen in Figure 1. These are the initial coefficients values seen in Table 2 and Table 3.

The Q_h,ref and Power_h,ref quantities in eq. (15) and eq. (16), respectively, were taken to be the largest condenser heat transfer rate (43,890 W) and compressor power draw (12,625 W) found in the lab testing data. These values were input into the auxiliary program and model as such. ${\dot{V}}_{\text{L,ref}}$ was taken to be the constant lab test data condenser flow rate, 0.00166 m³/s and ${\dot{V}}_{\text{S,ref}}$ was taken to be 0.00662 m³/s, the typical recommended T-10 wastewater solids handling pump flow rate.

As mentioned in the Lab Test Performance Data section of this manuscript, the condenser and evaporator side flow rates were held constant during testing. It was therefore deemed inappropriate to calibrate the heat pump object based on constant flow conditions to then simulate with potentially varying flow rates. To correct this inconsistency, the initial values of C4 and C5 were combined with C1. Coefficients C4 and C5 were then set to zero. This was repeated for the D coefficients. The adjusted coefficients can be seen in Table 2 and Table 3. Figure 4 displays the EnergyPlus heat pump performance using Equations 15-18 with the adjusted coefficients overlaid on the original lab test data. The graph shows that the model's heat pump object is well calibrated to mimic the actual performance of the T-10 unit.

Figure 4.

Original lab test data and adjusted EnergyPlus heat pump performance

The Energy Management System (EMS) is a ‘high level, supervisory control' feature of EnergyPlus [17]. EMS involves creating customized programs using the EnergyPlus Runtime Language. For this model, EMS was used to transmit virtual wastewater from the DHW loop to the WW-Water loop. This EMS work around is required because the domestic water connections object is an open loop and E+ does not allow for open loop water to be easily incorporated into a closed plant loop. EnergyPlus calculates the demanded flow rates for both hot and cold water, as well as the resultant wastewater temperature. Figure 5 is a diagram of a generalized domestic water connections object from the EnergyPlus Input-Output Reference [18] (p 2200). The built-in WWHR feature seen in Figure 5 attempts to capture the effects of a drain pipe wrap gravity film heat exchanger coil and is not flexible enough to model a heat pump system properly. The built-in heat recovery feature can only recover heat for pre-heating water at the fixture. If this feature was used in the model, it would not properly capture the temperature and mass flow rate profiles seen by the T-10 heat pump. Thus, EMS was used.

Figure 5.

EnergyPlus WaterUse:Connections diagram [14] (p 1510)

An EMS program was written to use the wastewater outlet temperature and drain water mass flow rate to control the flow of water to the WW-Water plant loop (Figure 6). This loop connects the WW tank to a temperature source object. The temperature source object outputs water at a scheduled temperature without using any virtual fuel resources [18] (p 832). Not using any virtual fuel resources allows for accurate accounting of energy transfers during simulations since, in reality, no resources are required to generate the warm wastewater in the storage tank. EMS resets the scheduled output temperature and mass flow rate of the temperature source object to match the calculated drain water temperature from the domestic water end uses at each timestep. The result is that the WW-Water loop demand side, which contains the WW tank, sees water with the same temperature and mass flow rate as actual wastewater entering the tank.

Figure 6.

EMS solution to the WaterUse:Connections challenge

To prevent redundancy, Table 4 displays model inputs that were consistent across several objects. Table 5 displays where inputs deviated from those in Table 4.

Several parameters were set to autosize. This was done to allow for models to run without capacity constrictions. The linear performance curves in the components that were autosized prevented this model flexibility from distorting the results. Additionally, some objects were modelled that are not present in an installed system. An example is the WW-Water loop. As described in the Energy Management System section, this loop was added to improve model fidelity. Thus, the loop pump power was set to zero to prevent accounting for excess energy consumption.

Economic viability was established using Simple Payback Period (SPP). This simple economic metric is calculated using eq. (19) where CF₀ is the annual net cost savings and I is the initial investment [21]:

$\text{SPP}=\frac{{\text{CF}}_0}{\text{I}}$ (19)

SPP is measured in years and does not account for the time value of money, but provides a good proxy for the quality of the investment opportunity. The shorter the SPP, the more attractive the investment is. For context, a 2010 white paper by Siemens displayed the results of an energy efficiency survey in which only 21% of respondents were willing to accept a payback of longer than four years, while 39% of respondents required a shorter payback period to invest in a project [22]. The remaining proportion used other metrics such as internal rate of return to make decisions.

The Large hotel model features 11,345 m² of conditioned spaces in six above-ground floors and a basement. The baseline model consumes 44,333 m³ of domestic hot and cold water per annum, which results in a large volume of wastewater. Baseline model water heating is done with 80% efficient natural gas-fired boilers with a supply water setpoint of 60 °C. The average utility costs are seen in Table 6 [24].

The Mid-rise apartment model features 3,135 m² of conditioned spaces in 4 above-ground stories. The baseline model consumes 1,875 m³ of domestic hot and cold water per annum. Baseline model water heating is done with 80% efficient natural-gas fired boilers with a supply water setpoint of 60 °C. The average utility costs are seen in Table 7 [24].

Table 8 shows the results of the 8 Large hotel simulations. Significant savings were seen in all climate zones and categories. The greatest energy savings percentage occurred in climate zone 4C-Seattle. The greatest energy cost savings occurred in climate zone 7A-Duluth. Also, the greatest GHG reduction by percentage occurred in Seattle, while the greatest absolute GHG reduction occurred in Duluth.

The cost of a WWHR system for a typical large hotel with the specifications of the reference model was estimated by the T-10 manufacturer to be about USD 150,000. This estimate was for WWHR equipment only and did not include installation, design or other equipment. Nevertheless, USD 150,000 was used to estimate SPPs. Duluth featured the shortest SPP of 4.0 years. Figure 7 shows Energy Use Intensity (EUI) reductions by end use for the entire building.

Figure 7.

Large hotel model EUI and energy end-use reductions

Table 9 shows the results of the 8 Mid-rise apartment simulations. In contrast to the Large hotel results, savings were either minimal or non-existent across almost all categories. In fact, GHG emissions increased in 3 out of 4 climate zones. The greatest EUI reduction occurred in climate zone 7A-Duluth. The greatest energy cost savings occurred in climate zone 5B-Boulder and the greatest GHG reduction occurred in Seattle, which was the only climate zone that resulted in a GHG reduction.

The cost of a WWHR system for a Mid-rise apartment was estimated by the T-10 unit manufacturer to be about USD 80,000. This estimate only includes the cost the T-10 heat pump units and does not include installation, design, other pieces of equipment in the DHW production system or taxes. Nevertheless, USD 80,000 was used to estimate SPPs. All payback periods were unacceptably long with Boulder having the shortest at 73 years. Figure 8 shows EUI reductions by end use for the entire building.

Figure 8.

Mid-rise apartment model EUI and energy end-use reductions