Optimization of Toilet Bowl Ventilation technology for odor control and energy efficiency enhancement in public toilet

doi:10.21203/rs.3.rs-4840231/v1

Download PDF

Article

Optimization of Toilet Bowl Ventilation technology for odor control and energy efficiency enhancement in public toilet

https://doi.org/10.21203/rs.3.rs-4840231/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Public toilets frequently use considerable amounts of ventilation to ensure appropriate air quality while operating. This obviously results in energy loss and reduces the thermal comfort of the area in the winter. To optimize and improve the ventilation of traditional toilets, this paper uses the computational fluid dynamics (CFD) simulation methods to qualitatively and quantitatively analyze the performance of toilet bowl ventilation (TBV) technology under different airflow, odors, and commode models, as well as to compare the exhaust effect of different ventilation schemes and the energy-saving performance of TBV technology. The wind direction for both models was toward the toilet’s inside. Even if the highest mass concentration above the allowable limit, the iso-surface demonstrates that all extra odor volume is controlled inside the toilet bowl. The results show that the application of TBV technology in public toilets can reduce the airflow to 10m³/h during the toilet used and still meet the air quality and energy-saving requirements. This method has a thermal energy saving efficiency of 8.2W/°C. This investigation may efficiently reduce air heat dissipation caused by the ventilation process and fan power consumption while assuring effluent discharge, thereby establishing a foundation for the promotion and use of the TBV technology.

Toilet Bowl Ventilation (TBV)

Odor

Energy efficiency

Computational Fluid Dynamics (CFD)

Avoiding toilet odors is a critical issue that should be addressed in toilet design (Kimura et al., 2019; Luo et al., 2023). Feces is a substance that produces a strong malodorous odor. The urinal’s trapped pee and excrement will smell bad while every toilet area is in use (Basha, Hanlon, Stringfellow, & Camarillo, 2013). The smell will permeate the entire toilet area and possibly beyond if they are not cleaned up promptly. The primary sources of toilet odor are areas in the toilet where feces flow through, such as the septic tank and urinal (Chung, Lin, Yang, & Lai, 2019). Odors can also come from garbage cans in the toilet area and dirt left on the floor. The spread of toilet scents can be easily stopped by controlling the sources of odors when using the restroom (Afful, Oduro-Kwarteng, Antwi, & Awuah, 2016; Mitsuda, Ohsako, & Isoda, 1997). The conventional method of ventilating independent public restrooms to prevent odors can result in a considerable loss of heat (Malkawi, Yan, Chen, & Tong, 2016; Sinha, Yadav, Verma, Murallidharan, & Kumar, 2021; J.-H. Yang & Kim, 2016). Additionally, it is essential research topic to solve the energy-saving and comfort-demanding demand during the ventilation process of these restrooms (Laverge, Van Den Bossche, Heijmans, & Janssens, 2011; Zhao, Liu, & Ren, 2018). The primary factors influencing the comfort-demanding factor of these restrooms include air quality, thermal comfort, sound insulation, space brightness, etc. (T. Zhang, Su, Wang, & Wang, 2018). The energy-saving demand is primarily linked to thermal comfort demand and space brightness. According to the requirements (J. Liu, Li, Chen, Qian, & Zhang, 2023; MOHURD, 2012), under the heating condition, the thermal comfort temperature of the personnel’s short-term stay area should reach 16℃ at least, and the non-heating duty room should not be lower than 5℃, the relative humidity should be ≥ 30%, and the air velocity should be ≤ 0.2m/s. The short-term stay location should have a maximum temperature of 30℃, a relative humidity of 40 ~ 70%, and the air velocity of ≤ 0.3m/s under cooling conditions (Cuce et al., 2019).

Scholars frequently utilize modelling and experimental approaches to research toilet ventilation, odor control, and energy conservation (English, 2020). Toilet ventilation is a prominent way to control odor diffusion. Yun-Chun Tung et al. (Tung, Hu, & Tsai, 2009) found that adjusting the frequency of air changes had a bigger impact on odor eradication than adjusting the position of the commode. In terms of energy conservation, the 8.5 ACH air change ratio proved to be the most efficient. Caiqing Yang et al. (C. Yang, Yang, Xu, Sun, & Gong, 2009) indicated that a top air supply combined with back wall exhaust was the best ventilation plan for the toilets of a clean room. Y.A. Ao et al. (Yongan, Li, Xin, & Chao, 2011) indicated that, in comparison to other situations, the range of influence of pollutant diffusion is drastically reduced in the toilets including a commode and side exhaust. Z.H. Zhang et al. (Z. Zhang, Zeng, Shi, Liu, et al., 2021; Z. Zhang, Zeng, Shi, Yang, et al., 2021) found that, longer air ages and reduced air change ratio could arise from increasing the number of air change per hour. Some researchers have also looked into the commode and low-level exhaust to improve ventilation. Using the CFD method to eliminate odors, M. Kimura et al. (Kimura et al., 2019) examined the toilet ventilation performance, and indicated that regardless of toilet type, effectively reduced ammonia concentrations. Youngjin Seo et al. (Seo & Seouk Park, 2013) investigated a toilet bowl ventilation (TBV) system mounted on a toilet seat using CFD methods. The study’s findings demonstrated that, regardless of the size or quantity of holes, the concentration of contaminated odors could be kept below 40ppm in a range of scenarios involving 2 ~ 20 pairs of open holes. The size of the exhaust airflow has an impact on the flush toilet’s internal airflow. The two pairs of hole placements at the back of the commode have the best air venting effect when the suction hole size is intended to be 4×4mm².

Software analysis and experimental validation have been combined in studies about energy-efficient ventilation design for toilets (Z. Zhang et al., 2022). Software analysis frequently makes use of CFD programs like AIRPAK or FLUENT (Chenari, Dias Carrilho, & Gameiro da Silva, 2016). The conventional toilet ventilation method needs to be optimized and improved to ensure the impact of sewage disposal, minimize the power consumption of the fan, and effectively reduce the air heat dissipation generated by the ventilation process. This work achieves the objective of lowering energy consumption in independent public toilets while also taking the sewage effect and ventilation efficiency into consideration. Independent public toilets satisfy the criteria for energy efficiency, thermal comfort, and air quality, but additional study is still needed to fully understand the innovative design of toilet bowl ventilation (TBV) technology. This study mainly examines three aspects of TBV, pollutability, ventilation, and comfort, respectively. Pollutability mainly examines the control effect of the exhaust technology on the diffusion of pollutant gases, with the concentration of pollutant gases as the investigation index. Ventilation mainly examines the effect of this ventilation technology to replace the traditional ventilation technology, with the average wind speed as the investigation index. Comfort mainly examines the impact of the use of this ventilation technology on the comfort of users, using the maximum wind speed as an indicator. (Behrendt et al., 2002; Jan Hennigs et al., 2021; J. Hennigs et al., 2021)

The paper is structured as follows. The following section involves setting up the necessary conditions for the simulation study, which includes figuring out the parameters of the odor indicator, optimizing the technology for TBV, creating a CFD simulation model for the TBV, and conducting experimental verification. Section 3 is to carry out the simulation study: qualitative and quantitative analysis of the simulation results of different airflow, different odors and distinctive toilet models, and analysis of the energy-saving performance of the TBV technology. Section 4 concludes the significance of the work in this paper.

2.1 Determine odor indicator parameters

CFD is an interdisciplinary discipline that uses computer and fluid mechanics knowledge to help engineers solve flow-related design and technical problems (Zhuang, Li, & Tu, 2013)(Zhuang et al., 2013)(Perén, van Hooff, Leite, & Blocken, 2015). According to a study on the composition of volatile chemicals in human excreta (Palit, Lunniss, & Scott, 2012; Sato, Hirose, kimura, Moriyama, & Nakashima, 2001), the most common volatile substances with odor in excreta are fatty substances, sulfur-containing compounds, and nitrogen-containing compounds (Palit et al., 2012; Sato et al., 2001). The largest proportions in each category (Ye et al., 2017) are, respectively, acetic acid (CH₃COOH) concentration reaches 40 ~ 120ppm, accounting for about 65% of the proportion of volatile substances; hydrogen sulfide (H₂S) concentration is 19 ~ 50ppm, accounting for about 1.6%; and ammonia (NH₃) concentration is 18 ~ 34ppm, accounting for about 6.5% of the proportion of volatile substances. H₂S and NH₃ are mainly analyzed and discussed in this study. The sensory thresholds for these two substances are 4.1×10^− 4ppm (mass concentration of about 7×10^− 4 mg/m³) for H₂S and 1.5ppm (mass concentration of about 1.14mg/m³) for NH₃ (Z. Zhang, Zeng, Shi, Yang, et al., 2021). The mass flow rate values of H₂S and NH₃ utilized in this study can be estimated by combining the density values of the two odors, as the release rate of odors is typically set to 0.3L/min (Tung et al., 2009). The diffusion coefficient between NH₃ and air at atmospheric pressure (1.013×10⁵Pa) is 1.98×10^− 5m²/s. The diffusion coefficient between H₂S and air is calculated using Fuller’s formula (Chengjing & Shaoyi, 2010). Table 1 displays the parameters of the two odors that were present in the toilets used in this study: H₂S and NH₃. The odor intensity is often divided into 6 levels using the odor qualitative analysis method (General Administration of Quality Supervision, 2022). The maximum permissible values of pollutant concentrations in the highest standard class of flush public toilets specified (Chappuis, Niclass, Cayeux, & Starkenmann, 2016; Cheng, Kwok, Li, Tong, & Lau, 2021; Regulation, 2021) are that the mass concentration of H₂S cannot exceed 0.01mg/m³, and the mass concentration of NH₃ cannot exceed 0.3mg/m³.

Table 1

Setting the parameters for the odors in toilet.
Setting the parameters for the odors in toilet.
Parameter Type	Unit	H₂S	NH₃
Density	kg/m³	1.189	0.771
Molar mass	g/mol	34.08	17.031
Airflow	L/min	0.3	0.3
Mass Flow	kg/s	5.945×10^− 6	3.855×10^− 6
Mass fraction	-	1.41×10^− 4	7.06×10^− 4
Concentration	mg/m³	170.16	85.20
Diffusion coefficient	m²/s	1.414×10^− 5	1.98×10^− 5

2.2 Optimized design of toilet bowl ventilation

The most frequent types of toilets used nowadays are sitting toilets and squatting toilets, which are designed to mimic human sitting and squatting postures. The fecal-urine separation technology has become popular in the application. The design of TBV in this study is mostly based on both. This study designed the sitting toilet as a fecal-urine mixing toilet because of the small opening, which can cause the odor diffused via the two excretory bowls to mix quickly even with fecal-urine separation technology. The squatting toilet differs from the previous situation in that the toilet’s opening is completely open, fecal-urine separation technology is used, and the two excretory bowls are relatively far apart (Lopes & Costa, 2019), affecting the diffusion effect of contaminated odors, so the squatting toilet is designed as a fecal-urine separator in this study.

The design of the TBV used in this study is primarily based on the design created by a Korean research team (Seo & Seouk Park, 2013), who imitated using a sitting toilet as a background while conducting their investigation. Following study and simulation, it was threefold optimized: 1) Disconnect the end of the annular ventilation pipe (away from the fan end) to prevent symmetrically connected parts from canceling each other’s air velocities (Fig. 1); 2) Shorten the length of the vent pipe approximately possible; 3) Consider processing convenience and structure robustness.


Figure 1. Diagram of disconnection at the end of the annular ventilation pipe (away from the fan end). (a) Annular model (b) End disconnection model

In the design of the ventilation pipe, the cross-sectional area of the entire ventilator pipe remains unchanged. To ensure that the ventilation effect is optimal while minimizing the number of holes, the proposed 4 types of ventilation pipe programs are chosen: 4 holes (2 holes on one side), 8 holes (4 holes on one side), 12 holes (6 holes on one side), and 16 holes (8 holes on one side). The 4 types of ventilation pipe programs are shown in Fig. 2.

Figure 2. The ventilation pipe design (Sitting toilet as an example).

(a) 4 holes (2 holes on one side) (b) 8 holes (4 holes on one side)

As the number of apertures increases, the dispersion of air velocity diminishes. In practice, the number of pipeline openings should be kept to a minimum to avoid low efficiency at the pipeline’s end. The 12-hole (6 holes on one side) and 16-hole (8 holes on one side) programs are not suitable for the application due to the large number of openings. The 4-hole program (2 holes on one side) has fewer apertures, but the distance between the holes is too long, resulting in an excessively high local air velocity. In this work, the 8-hole (4 holes on one side)) system is used to build the TBV pipe for later simulation, and the vent hole layout criteria in the subsequent squatting toilet simulation are also based on this model. The ‘8-hole (4 holes on one side) program’ (Fig. 2-(b)) of the openings from front to end of the inlet in order 1 ~ 4.

2.3 Geometric modeling and meshing

This study creates a 0.9m×0.9m×0.9m cube environment to better represent the performance of TBV technology. The height of the model is where the human body breathes when toileting. The sitting toilet model (Fig. 3a b c) follows the standard toilet shape, with a semicircular front end and straight sides and ends. Ventilation ducts are symmetrically placed on the left and right sides of the interior of seat. The squatting toilet model (Fig. 3d e f) is based on standard potty proportions and was created with fecal-urine separation in mind (Chen, Sari, Liao, & Lin, 2021).

Figure 3. Ventilation performance simulation model.

(a) Sitting toilet Y section (b) Sitting toilet X section (c) Sitting toilet Z section

(d) Squatting toilet Y section (e) Squatting toilet X section (f) Squatting toilet Z section

The squatting toilet ventilator is set up similarly to the sitting toilet, with an inner overhanging section that is about 30mm broad and utilized to arrange the ventilation holes. Ventilation pipes are installed on the symmetrical left and right sides. To further examine and interpret the simulation findings, three cross-sections were determined, as illustrated in Fig. 3. The Y section was positioned in the model’s symmetric cross-section, with the X and Y sections perpendicular to each other in the horizontal direction, and the Z section was 10mm below the upper skin of the opening part of each commode.

Because the simulation models of the two toilets have the same size and the difference in detail dimensions is minor, the sitting toilet is chosen as a representative for grid independent verification. After determining grid independence, the squatting toilet model meshing approach also considers sitting toilet operation. By adjusting the grid cell control size parameter of the sitting toilet simulation model, eight distinct groups of grid division models with grid numbers ranging from 0.3 million to 2.5 million were created. Following simulation, the average air velocity of the toilet opening cross-section is compared to the simulation results of each model, and the simulation results are fitted as illustrated in Fig. 4. The fitted value tends to flatten when the number of grids exceeds 1.5 million. The ultimate choice of the number of grids was 1.74 million sitting toilet grid models in order to guarantee the accuracy of the simulation values and account for the computation’s duration. Because the sitting toilet and squatting toilet models are comparable, the sitting toilet’s grid cell size control parameters are also used to determine the number of squatting toilet grids. Finally, the squatting toilet simulation requires approximately 1.7 million grids. Figure 5 shows the specific grid division of the two models.


Figure 4. TBV performance mesh independence validation.


Figure 5. Meshing for TBV model. (a) Sitting toilet Model (b) Squatting Toilet Model

2.4 Boundary conditions and working cases

In this study, the turbulence model is chosen as the Realizable k-ε model. Since this study involves the diffusion of odor, the component transport model is opened in the simulation calculation. This study uses a different simulation approach since the little space inside the TBV exhaust piping causes a large mesh difference and number when integrated with the toilet and external models, which hinders the computations’ ability to converge. The sitting toilet and squatting toilet’s diffusion simulation model’s shape is established beforehand in the simulation. Next, calculate the air velocity at each opening site under each case (cases 1 ~ 5 for the sitting toilet model and cases 6 ~ 10 for a squatting toilet model) using the exhaust piping model. In order to simulate the process, the contaminated gas diffusion model was updated with the acquired air velocity data.

Initially, the sitting vent pipe was simulated in the ventilation performance simulation. The following stage of the TBV performance simulation is then carried out after obtaining the average air velocity values for every open segment of the vent pipe.

The following boundary conditions are used in this study to simulate the diffusion of odors in the toilet model: 1) Outflow boundary conditions: because it is a 1/2 model, set the cross-section of the opening site for the mass flow outlet boundary conditions.; 2) Free inlet: designate the Pressure inlet boundary condition as the location of the top surface of the 0.9m×0.9m×0.9m cube; 3) Pollutant release boundary conditions: the NH₃ release mass flow rate of the toilet bowl is 3.855×10^− 6kg/s and the mass flow rate of H₂S discharge is 5.945×10^− 6kg/s; 4) Symmetry interface boundary condition: set the symmetry cross-section of 1/2 model as Symmetry boundary condition; 5) Wall boundary conditions: set the other interface around the cube.

There are 5 cases in the sitting toilet bowl and the squatting toilet ventilation pipeline model simulation process, including no air velocity cases and 4 different types of airflow in the exhaust boundary conditions (Mass flow outlet) settings. As indicated in Table 2, the primary setting principles are to toilet 40m³/h airflow for the maximum value, with each case flow gradually reducing. Table 3 displays the air velocity distribution analysis of the toilet ventilator for each hole. It is evident that variations in ventilation had no effect on the air velocity stability of any hole, as evidenced by the comparatively tiny differences in each open hole’s maximum, minimum, and average values.

Table 2

Simulated cases for sitting and squatting toilet model.
Simulated cases for sitting and squatting toilet model.
Case of sitting model	Case of squatting model	Airflow	Mass Flow	Odor	Mass fraction
Case of sitting model	Case of squatting model	m³/h	kg/s		-
Case 1	Case 6	10	1.792×10^− 3	NH₃-Air	0.706×10^− 4
Case 1	Case 6	10	1.792×10^− 3	H₂S-Air	0.141×10^− 3
Case 2	Case 7	20	3.583×10^− 3	NH₃-Air	0.706×10^− 4
Case 2	Case 7	20	3.583×10^− 3	H₂S-Air	0.141×10^− 3
Case 3	Case 8	30	5.375×10^− 3	NH₃-Air	0.706×10^− 4
Case 3	Case 8	30	5.375×10^− 3	H₂S-Air	0.141×10^− 3
Case 4	Case 9	40	7.167×10^− 3	NH₃-Air	0.706×10^− 4
Case 4	Case 9	40	7.167×10^− 3	H₂S-Air	0.141×10^− 3
Case 5	Case 10	0	0	NH₃-Air	0.706×10^− 4
Case 5	Case 10	0	0	H₂S-Air	0.141×10^− 3

Table 3

Air velocity at each hole of the sitting and squatting toilet model.
Air velocity at each hole of the sitting and squatting toilet model.
Inlet	Case of sitting model				Case of squatting model
	Case	`v	v_max	v_min	Case	`v	v_max	v_min
		m/s	m/s	m/s		m/s	m/s	m/s
Inlet 1	Case 1	1.07	1.23	0.52	Case 5	1.17	1.34	0.57
Inlet 2		2.39	2.80	1.39		2.59	3.00	1.56
Inlet 3		2.54	2.96	1.45		2.29	2.69	1.29
Inlet 4		1.06	1.22	0.49		1.02	1.18	0.48
Inlet 1	Case 2	2.20	2.44	1.37	Case 6	2.40	2.67	1.48
Inlet 2		4.78	5.49	3.32		5.17	5.89	3.70
Inlet 3		5.11	5.82	3.50		4.61	5.30	3.12
Inlet 4		2.20	2.45	1.31		2.12	2.37	1.28
Inlet 1	Case 3	3.33	3.64	2.32	Case 7	3.63	3.99	2.51
Inlet 2		7.17	8.16	5.35		7.77	8.79	5.95
Inlet 3		7.71	8.70	5.72		6.92	7.89	5.07
Inlet 4		3.35	3.68	2.26		3.22	3.54	2.21
Inlet 1	Case 4	4.51	4.82	3.32	Case 8	4.86	5.29	3.60
Inlet 2		9.65	10.81	7.42		10.40	11.70	8.28
Inlet 3		10.41	11.59	8.00		9.24	10.47	7.07
Inlet 4		4.59	4.90	3.28		4.32	4.70	3.19

2.5 Experimental verification

This study carried out an experimental validation of the TBV duct design plan in order to confirm the simulation’s accuracy. The Korean research team’s (Seo & Seouk Park, 2013) design plan for the TBV duct with tail exhaust was used as a reference for control purposes during the validation process. The team validated the contaminated gas diffusion model using experiments, and the results showed that the model can accurately predict the concentration distribution of contaminated gases in the toilet space. This provides a reliable basis for numerical simulations using the model. By setting the same parameters as the literature experiment for simulation, we can get a comparison graph between the simulated values and the literature experiment values (Fig. 6). The results show that both values and trends are close to each other for the concentration of pollutant gases and the error is within acceptable limits.


Figure 6. Comparison of literature experimental value and simulated value.

A compact toilet exhaust space unit of 0.9m×0.9m×0.9m was established during the experiment. An identical-sized sitting toilet model and an exhaust line that resembled a toilet seat were placed in the area. The exhaust line connected to a fan outside the space unit and converged at the back of the simulated toilet. Light-transmitting plastic sheet covered the space’s bottom surface and 4 walls, leaving the top surface unobstructed. This is designed to allow additional air to enter the area during exhaust while also preventing the surrounding air from unduly disturbing the gaseous environment within the space. The air outlet’s cross-sectional size and opening area had proportions that were similar to those of the simulation setup. The cross-section is simplified to a rectangular shape to facilitate the fabrication of the exhaust air piping. The cross-sectional area is 1×10^− 3m². The fan ventilation is kept at the same 30m³/h during the experiment as it is in the simulation.

Each open section’s air velocity was measured for the trials using an anemometer. The anemometer used was a high-precision thermal anemometer (Model 6006-2C, accuracy ± 0.01m/s, provided by KANOMAX Kano Max, Japan) to record. The study selected the ‘inlet 2 ~ 4’, which is near the air outlet and has relatively stable data, as the validation point for data collection because the anemometer’s high sensitivity makes it possible for the air velocity to fluctuate significantly during data collection. To determine the average air velocity for a specific opening location, 6 data points were collected at the left, center, and right positions of each air vent verification point, for a total of 18 data points. These data points were then weighted and averaged (Fig. 7).

Table 4 can be organized following the statistics and organization of the experimental data. By means of comparison, it is discovered that inlet 2 has the biggest inaccuracy, reaching 0.06m/s, and the error rate is around 10% in the air velocity distribution of the positions of openings 2~4. There is positive agreement between the data of inlet 2, 3, and 4’s opening cross-sections. This experiment indicates that the toilet bowl exhaust can be simulated using a computer model.



Figure 7. Toilet exhaust design experiment device.

Table 4

Comparison of simulated and experimental values.
Inlet	`v (Simulated value)	`v (Experimental value)	Relative error value
	m/s
Inlet 2	0.61	0.55	± 0.06
Inlet 3	2.50	2.55	± 0.05
Inlet 4	11.11	11.15	± 0.04

This section of the study examines how various air velocities affect the simulation findings and analyzes the results of two odors: H₂S and NH₃. For this investigation, two nephogram forms that the vertical (horizontal)-X cross-section and the iso-surface of the model (1/2 model) that were used to facilitate a more thorough analysis of the simulation results of various odor compositions.

The upper limit value of odors needed by the standard is referred to as the iso-surface in this study, which is an equivalent surface that forms a volume area higher than or equal to this value. The concentration distribution is displayed as a nephogram along the area’s boundaries. Which areas have pollutant concentrations over the acceptable limit can be adequately represented by the volume. Subsequent analysis of the results of the simulation of the composition of odors is also used in the form of the nephogram.

3.1 Simulation results for different airflow

The comparison in Fig. 8 shows that the wind direction at the cross-section of the toilet bowl opening is toward the interior of the model and that only the air velocity changes as the airflow increases. The air velocity in the toilet bowl opening cross-section for cases 2 ~ 4 all have locations over 0.25m/s, according to the simulated horizontal-Z cross-section air velocity for the toilet bowl. Figure 9 compares the Z-section wind vectors for the 5 cases in the squatting toilet model. The air velocity in the toilet bowl opening cross-section of cases 7 ~ 9 have a location of more than 0.25m/s, according to the horizontal-Z cross-section air velocity of the squatting toilet.

Except for cases 5 and 10, which have no airflow, the other 8 cases (cases 1 ~ 4 and 6 ~ 9) are able to meet the ventilation requirements. The wind direction over the toilet bowl opening is directed inside when the exhaust air is engaged (airflow not less than 10m³/h). In accordance with the comfort standards, the sitting toilet model case 1 (airflow for 10m³/h) at the opening cross-section of the air velocity does not exceed 0.25m/s. Cases 2 ~ 4 all surpass this amount. Since the toilet comes into close contact with human skin during the application process, intelligent control mechanisms may be taken into consideration. In human contact with the toilet when ventilation is decreased or stopped in order to satisfy comfort standards. The air velocity at the entrance section of the squatting toilet model case 6 does not exceed 0.25m/s, meeting the comfort criteria, whereas cases 7 ~ 9 surpass this value.

Since the squatting toilet is situated at a set distance from the human body during the practical application, the air velocities that are excessive at the partially puny for opening cross-section will not affect its use. It is possible to take into account the airflow management in 20m³/h to satisfy comfort needs while accounting for sewage requirements. The TBV is surrounded by high winds, while the center section of the air velocity distribution is near a significant attenuation. The features of the air velocity are primarily concentrated in this location.


Figure 8. Wind direction simulation nephogram for sitting toilet model (horizontal-Z). (a) Case 1–10m³/h (b) Case 2–20m³/h (c) Case 3–30m³/h (d) Case 4–40m³/h (e) Case 5 - zero


Figure 9. Wind direction simulation nephogram for squatting toilet model (horizontal-Z). (a) Case 6–10m³/h (b) Case 7–20m³/h (c) Case 8–30m³/h (d) Case 9–40m³/h (e) Case 10 - zero

3.2 Simulation results for different odors

Figure 10 displays the distribution of NH₃ concentration in each of the 5 cases (cases 1 ~ 5) of the TBV model. The volume of the gas mixture with an NH₃ mass fraction of 2.5×10^− 7 (mass concentration of around 0.3 mg/m³) was set as the iso-surface in the figure and studied in order to more clearly determine the NH₃ diffusion distribution under each circumstance. Figure 11 displays the distribution of ammonia concentration for the 5 cases (cases 6 ~ 10).


Figure 10. NH₃ diffusion simulation nephogram for sitting toilet model (iso-surface). (a) Case 1–10m³/h (b) Case 2–20m³/h (c) Case 3–30m³/h (d) Case 4–40m³/h (e) Case 5 - zero


Figure 11. NH₃ diffusion simulation nephogram for squatting toilet model (iso-surface). (a) Case 6–10m³/h (b) Case 7–20m³/h (c) Case 8–30m³/h (d) Case 9–40m³/h (e) Case 10 - zero

Figure 12 displays the distribution of H₂S concentration in the TBV model under 5 different cases (cases 1 ~ 5), where H₂S is used as the odor. The volume of the mixture with the mass fraction of H₂S equal to 8.0×10^− 9 (the mass concentration is equal to about 0.01 mg/m³) is set as the iso-surface in the figure, and the analysis is carried out in order to more clearly judge the diffusion distribution of H₂S under each case. Figure 13 displays the H₂S concentration pattern for 5 cases (cases 6 ~ 10).


Figure 12. H₂S diffusion simulation nephogram for sitting toilet model (iso-surface). (a) Case 1–10m³/h (b) Case 2–20m³/h (c) Case 3–30m³/h (d) Case 4–40m³/h (e) Case 5 - zero


Figure 13. H₂S diffusion simulation nephogram for squatting toilet model (iso-surface). (a) Case 6–10m³/h (b) Case 7–20m³/h (c) Case 8–30m³/h (d) Case 9–40m³/h (e) Case 10 - zero

The following can be discovered after the results are analyzed. The highest average values of NH₃ and H₂S concentrations in the opening cross-section of the sitting toilet model are 2.88×10^− 3mg/m³ and 4.87×10^− 3mg/m³, respectively, when the airflow is between 10m³/h and 40m³/h, comparing the working cases. In the open cross-section of the squatting toilet model, the maximum average concentrations of NH₃ and H₂S were 3.53×10^− 3 mg/m³ and 2.52×10^− 3mg/m³, respectively, but they were all within the acceptable ranges. All gas quantities that exceeded the iso-surface were regulated within the toilet bowl opening. Since the wind is blowing entirely in the direction of the inside of the toilet bowl opening, spilling won’t occur even if the nearby concentration of odors exceeds than allowed. In the no-airflow cases (cases 5 and 10), the concentration of odors was significantly greater in the sitting toilet model than in the squatting toilet model. The H₂S has dispersed more equally across the entire model. It’s possible that the concentration of NH₃ in front of toilet bowl opening is correlated with the direction in which it travels.

The TBV technology can customarily be adjusted to an airflow of 40m³/h for optimal ventilation to guarantee air purity. Particularly if the contaminated gases are not limited to the inside of the toilet bowl, this airflow creates air velocities that guarantee the contaminated gases are extracted from the toilet bowl promptly. When using the toilet, the energy-saving and comfort-related ventilation technology may be adjusted to 10m³/h. Based on the results of the simulation, this setting can also control the spread of toxic gases in the toilet, ensuring air quality.

3.3 Quantitative analysis of ventilation performance

To further reflect the simulation results of the TBV performance, this study examines the average, maximum, and minimum values of air velocity and mass concentration at the toilet bowl opening (Z section) position, which are shown in Table 5.

In the sitting toilet model, it is evident from the air velocity that the most significant air velocity in cases 1 ~ 4 is around 3 times the average air velocity. Based on the average value calculation, every case satisfies the comfort standards. When it comes to mass concentration, the cross-section cases 1 ~ 4 average values meet the discharge performance requirement of less than 0.3mg/m³, but the maximum values rise as airflow increases while the average values are constant regularly. The mass concentrations of NH₃ in the data of the openings from cases 1 ~ 4 all displayed $\:\stackrel{-}{{C}_{p4}}<\stackrel{-}{{C}_{p3}}<\stackrel{-}{{C}_{p2}}<\stackrel{-}{{C}_{p1}}$. The sitting toilet model’s front entrance absorbed the largest concentration of odors, whereas the tail opening absorbed the least amount. (Fig. 2-b shows the locations of the openings.) The mass concentrations of H₂S in cases 1 ~ 4 match those of the NH₃ simulation, with $\:\stackrel{-}{{C}_{p4}}<\stackrel{-}{{C}_{p3}}<\stackrel{-}{{C}_{p2}}<\stackrel{-}{{C}_{p1}}$. The front entrance of the sitting toilet absorbed the most pollutants, whereas the tail opening absorbed the least.

Table 5

Table 5

Air velocity and mass concentration for toilet bowl opening cross-section; Average mass concentration for each exhaust hole cross-section.
Air velocity and mass concentration for toilet bowl opening cross-section; Average mass concentration for each exhaust hole cross-section.
		`v	v_max	v_min	`C	C_max	C_min	`C_p1	`C_p2	`C_p3	`C_p4
		m/s	m/s	m/s	mg/m³	mg/m³	mg/m³	mg/m³	mg/m³	mg/m³	mg/m³
NH₃	Case 1	0.05	0.16	4.68×10^− 4	2.69×10^− 3	0.28	0	0.58	0.44	0.36	0.21
	Case 2	0.12	0.34	7.83×10^− 4	2.88×10^− 3	0.38	0	0.48	0.33	0.24	0.1
	Case 3	0.18	0.52	1.32×10^− 3	2.79×10^− 3	0.43	0	0.47	0.3	0.21	0.08
	Case 4	0.24	0.72	1.95×10^− 3	2.53×10^− 3	0.46	0	0.47	0.29	0.2	0.07
	Case 5	1.26×10^− 3	5.40×10^− 3	2.56×10^− 5	4.46	25.17	0.05	-	-	-	-
	Case 6	0.05	0.14	1.15×10^− 3	3.53×10^− 3	0.14	0	0.78	0.29	1.13	1.29
	Case 7	0.11	0.3	1.91×10^− 3	8.57×10^− 4	0.13	0	0.56	0.14	0.74	0.82
	Case 8	0.17	0.46	3.04×10^− 3	5.02×10^− 4	0.09	0	0.5	0.1	0.67	0.7
	Case 9	0.23	0.63	4.62×10^− 3	2.64×10^− 4	0.06	0	0.47	0.09	0.62	0.62
	Case 10	1.26×10^− 3	4.49×10^− 3	3.53×10^− 5	9.23	46.47	0.38	-	-	-	-
H₂S	Case 1	0.05	0.16	4.05×10^− 4	4.37×10^− 3	0.53	0	0.95	0.74	0.61	0.32
	Case 2	0.12	0.34	9.09×10^− 4	4.81×10^− 3	0.75	0	0.9	0.62	0.46	0.18
	Case 3	0.18	0.52	1.60×10^− 3	4.87×10^− 3	0.88	0	0.92	0.58	0.42	0.15
	Case 4	0.24	0.72	2.03×10^− 3	4.42×10^− 3	0.92	0	0.92	0.55	0.4	0.15
	Case 5	1.33×10^− 4	3.03×10^− 4	9.37×10^− 6	104.25	112.25	98.72	-	-	-	-
	Case 6	0.05	0.14	1.01×10^− 3	2.52×10^− 3	0.33	0	2.18	0.65	1.88	2.14
	Case 7	0.11	0.3	1.90×10^− 3	1.52×10^− 3	0.28	0	1.29	0.3	1.47	1.61
	Case 8	0.17	0.46	3.05×10^− 3	9.00×10^− 4	0.19	0	1.08	0.22	1.34	1.4
	Case 9	0.22	0.63	4.63×10^− 3	4.43×10^− 4	0.12	0	1	0.18	1.24	1.25
	Case 10	2.10×10^− 4	1.23×10^− 3	8.82×10^− 6	145.61	162.8	133.87	-	-	-	-

In cases 6 ~ 9 of the squatting toilet, the maximum air velocity remains 3 times higher than the average. The average air velocity in cases 8 and 9 meets the criteria for air venting. The comfort of every case can satisfy the standards if it is determined using the average value. The cross-section in cases 6 ~ 9 of NH₃ meet the discharge performance criteria of less than 0.3mg/m³. The concentration distribution law can be generally expressed as $\:\stackrel{-}{{C}_{p2}}<\stackrel{-}{{C}_{p1}}<\stackrel{-}{{C}_{p3}}<\stackrel{-}{{C}_{p4}}$. As with the NH₃ simulation, the maximum values decrease as the airflow increases, but the average values remain constant in a predictable manner. Except for case 6, the concentration distribution pattern in cases 7 ~ 9 generally resembles $\:\stackrel{-}{{C}_{p2}}<\stackrel{-}{{C}_{p1}}<\stackrel{-}{{C}_{p3}}<\stackrel{-}{{C}_{p4}}$. As airflow increased, the mass concentration in each hole more than doubled compared to the sitting toilet model simulation findings, resulting in the same condition as in the NH₃ simulation.

3.4 Energy savings analysis

The building heat consumption indicator usually consists of two components: heat transfer through the envelope, air conditioning as well as air infiltration through door and window gaps. Because considerable amounts of ventilation must be performed to keep the air quality in public toilets, air infiltration contributes significantly to energy loss(SeppȨnen, 2008; Wang et al., 2022). Independent public toilet energy consumption must be kept to a minimum by minimizing this portion of energy loss through ventilation reduction, with the goal of maintaining the highest possible level of air quality. Eq. 1 illustrates the process for calculating the air infiltration heat consumption (Y. Liu, 2021) in an independent public toilet facility during the winter.

$$\:{q}_{INF}=\frac{\left({t}_{i}-{t}_{e}\right)\times\:\left({C}_{p}\bullet\:\rho\:\bullet\:N\bullet\:V\right)}{{A}_{0}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$

where,

$\:{q}_{INF}$ - air infiltration heat consumption per unit floor area, W/m²;

$\:{t}_{i}$ - average indoor calculated temperature of all rooms, ℃;

$\:{t}_{e}$ - average outdoor temperature during the heating period, ℃;

$\:{C}_{p}$ - specific heat capacity of air, calculated as 0.28W·h/(kg·℃);

$\:\rho\:$ - air density, calculated as 1.3kg/m³;

$\:{A}_{0}$ - toilet building area, m².

$\:N$ – air change ratio in the toilet, times/h;

$\:V$ – toilet room volume, m³.

The air change ratio, air exchange volume, and the differential in temperature between the inside and outside are all closely related to the air’s heat consumption, according to the formula. The volume of the restroom is equivalent to twice the building area, assuming that the restroom is 2m tall. Based on the 5 times/h toilet ventilation frequency, the heat consumption of air infiltration in the building will increase by 3.64W/m² for every 1°C increase in the temperature difference between the internal and outside sections of an independent public toilet. Significant heat loss occurs when independent public toilets with air conditioning are kept in excellent shape during the winter. Energy savings can be achieved and the heat consumption of air infiltration efficiently reduced by lowering the air change ratio in a way that maintains air quality.

Consequently, it can be concluded that case 11’s ‘Top inlet and Toilet outlet’ ventilation approach will effectively increase ventilation efficiency while maintaining air quality. The modeling results reported previously indicate that when the airflow is set at 10m³/h, TBV may effectively control the diffusion of odors. To achieve ventilation and energy savings, the ventilation volume can therefore be intelligently controlled by detecting the state of the toilet bowl, lowering the airflow when nobody occupies it, and sensibly minimizing the fan power consumption and heat loss.

This toilet computes its area in square meters and utilizes the TBV technology. After using the toilet, the airflow is adjusted to 40m³/h for 10 minutes, and then to 10m³/h for the remaining duration. By lowering airflow, this method can save heat at a rate of 8.2W/°C. Reducing the airflow can save at least 3 times the motor power consumption and have energy-saving advantages because the fan’s motor power is directly related to the airflow. throughout the winter, the public toilet’s calculated inside temperature is 12°C from 6:00 to 22:00, with 5°C throughout the remaining hours of operation. Based on temperature data from a normal meteorological year during the winter, it is estimated that Tianjin’s winter has a temperature differential of 33229.1°C·h. During the winter, the air infiltration heat consumption in public toilets can significantly save 1306.26 MJ.

This research uses CFD simulation methods to optimize the design, indicators of performance, and energy efficiency of the TBV technology. The research findings are summarized as follows.

A better technique to address the issue of uneven airflow distribution in the ventilating pipeline is to install ventilation pipes on both sides of the toilet. There shouldn’t be an excessive amount of exhaust holes in the ventilation pipe. In this study, the 8-hole (4 holes on one side) scheme was selected as the model of the ventilation pipe for the study.

TBV technology can reduce the airflow to 10m³/h during the public toilet used and still meet the energy-saving and comfort requirements. The airflow wind direction for both models was toward the interior of the toilet bowl and no spillage occurred.

Regarding the odors control, the sitting toilet bowl opening had the highest mean concentrations of H₂S and NH₃, 2.88×10^-3mg/m³ and 4.87×10^-3mg/m³, respectively. In the squatting toilet bowl opening, the maximum average concentrations of H₂S and ammonia NH₃ were found to be 2.52×10^-3mg/m³ and 3.53×10^-3mg/m³, respectively.

The rate at which this technology reduces airflow to save heat energy can reach 8.2W/°C in the instance of 10m³/h. To achieve the ventilation and energy-saving goal of independent public restrooms, Tianjin’s toilets may reduce air infiltration heat consumption by 1306.26MJ during the winter while also consuming 3 times less power from the fans.

In terms of maximizing ventilation, maintaining air quality, and enhancing energy-saving efficiency, the TBV technology can successfully address the three challenges faced by public toilets. It also plays an essential part in advancing research and encouraging the use of this technology.

Author Contribution

Z.Z. : Methodology, Formal analysis, Investigation, Writing - original draftL.Z. : Conceptualization, Supervision, Resources, ValidationQ. H. : Project administration, Writing - review & editingY.W. : Conceptualization, SupervisionY.S. : Visualization, Data curationD.B. : Investigation, Data curation

Acknowledgement

The financial supports provided by the National Graduate Enterprise Programme (Grant No. 140382019014), and the Tianjin Renai College Research (project ID HX18021) are gratefully acknowledged. We thank Dr. Chengyi Li, Dingkun Peng, Yuanlin Jing, Xufeng yu for their help in field measurements and date analysis.

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

Corresponding author：Zhonghua Zhao

E-mail address: [email protected]

Afful, K., Oduro-Kwarteng, S., Antwi, E. O., & Awuah, E. (2016). Odour impact determination of a communal toilet: Field measurement with panellists using dynamic plume method and dispersion modelling. Open Journal of Air Pollution, 5(1), 1-9.
Basha, E., Hanlon, J. S., Stringfellow, W. T., & Camarillo, M. K. (2013). Performance of sanitary sewer collection system odour control devices operating in diverse conditions. Water Science and Technology, 68(12), 2527-2533. doi:10.2166/wst.2013.492.
Behrendt, J., Arevalo, E., Gulyas, H., Niederste-Hollenberg, J., Niemlec, A., Zhou, J., & Otterpohl, R. (2002). Production of Value Added Products from Separately Collected Urine. Water science and technology : a journal of the International Association on Water Pollution Research, 46, 341-346. doi:10.15480/882.230.
Chappuis, C. J. F., Niclass, Y., Cayeux, I., & Starkenmann, C. (2016). Sensory survey of key compounds of toilet malodour in Switzerland, India and Africa. Flavour and Fragrance Journal, 31(1), 95-100.
Chen, Y. L., Sari, R. K., Liao, Y. H., & Lin, W. C. (2021). Optimal span between feet of public squat toilet based on anthropometric data and squatting stability assessment. Healthcare (Switzerland), 9(1). doi:10.3390/healthcare9010042.
Chenari, B., Dias Carrilho, J., & Gameiro da Silva, M. (2016). Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review. Renewable and Sustainable Energy Reviews, 59, 1426-1447. doi:10.1016/j.rser.2016.01.074.
Cheng, J. C., Kwok, H. H., Li, A. T., Tong, J. C., & Lau, A. K. (2021). Sensitivity analysis of influence factors on multi-zone indoor airflow CFD simulation. Science of The Total Environment, 761, 143298.
Chengjing, C., & Shaoyi, J. (2010). Principles of Chemical Engineering Next Edition 2nd Edition. Beijing, China: Higher Education Press (PRC).
Chung, S.-C., Lin, Y.-P., Yang, C., & Lai, C.-M. (2019). Natural Ventilation Effectiveness of Awning Windows in Restrooms in K-12 Public Schools. Energies, 12(12), 2414. doi:10.3390/en12122414.
Cuce, E., Sher, F., Sadiq, H., Cuce, P. M., Guclu, T., & Besir, A. B. (2019). Sustainable ventilation strategies in buildings: CFD research. Sustainable Energy Technologies and Assessments, 36, 100540.
English, T. R. (2020). Restroom Ventilation Schemes: Energy Implications. ASHRAE Transactions, 126(1).
General Administration of Quality Supervision, I. a. Q. o. t. P. s. R. o. C. (2022). Quality requirements and evaluation of tourism toilets (GB/T 18973-2022). In. Beijing, China: Standards Press of China.
Hennigs, J., Ravndal, K. T., Parker, A., Collins, M., Jiang, Y., Kolios, A. J., . . . Tyrrel, S. (2021). Faeces – Urine separation via settling and displacement: Prototype tests for a novel non-sewered sanitation system. Science of The Total Environment, 753, 141881. doi:https://doi.org/10.1016/j.scitotenv.2020.141881.
Hennigs, J., Ravndal, K. T., Parker, A., Collins, M., Jiang, Y., Kolios, A. J., . . . Tyrrel, S. (2021). Faeces – Urine separation via settling and displacement: Prototype tests for a novel non-sewered sanitation system. Science of The Total Environment, 753. doi:10.1016/j.scitotenv.2020.141881.
Kimura, M., Akimoto, T., Kato, S., Hirasuga, N., Sakamoto, Y., Yamakita, S., & Sakakibara, H. (2019). Investigation of Toilets with Reduced Ventilation Frequencies and Odor Simulation. Paper presented at the IOP Conference Series: Earth and Environmental Science 294.
Laverge, J., Van Den Bossche, N., Heijmans, N., & Janssens, A. (2011). Energy saving potential and repercussions on indoor air quality of demand controlled residential ventilation strategies. Building and Environment, 46(7), 1497-1503.
Liu, J., Li, Z., Chen, X., Qian, R., & Zhang, Y. (2023). Study on the Concentration of Contaminants in the Toilet of the Teaching Building of Henan University of Science and Technology. International Core Journal of Engineering, 9(6), 199-204.
Liu, Y. (2021). Building Physics (5th Edition). Beijing, China: China Architecture & Building Press.
Lopes, A. G., & Costa, V. A. (2019). Using CFD in The design process of a toilet bowl. Journal of Fluid Flow, Heat and Mass Transfer, 6, 1-8.
Luo, D., Huang, J., Zheng, X., Liu, F., Li, Y., Wang, Y., & Qian, H. (2023). Spread of flushing-generated fecal aerosols in a squat toilet cubicle: Implication for infection risk. Science of The Total Environment, 859, 160212. doi:https://doi.org/10.1016/j.scitotenv.2022.160212.
Malkawi, A., Yan, B., Chen, Y., & Tong, Z. (2016). Predicting thermal and energy performance of mixed-mode ventilation using an integrated simulation approach. Building Simulation, 9(3), 335-346. doi:10.1007/s12273-016-0271-x.
Mitsuda, M., Ohsako, M., & Isoda, N. (1997). A comparison of odor evaluation indicators based on odor threshold value and those based on unpleasantness of body odor, and their effectiveness in the management of indoor air quality. Journal of the Human-Environment System, 1(1), 65-71.
MOHURD, M. o. H. a. U.-R. D. o. t. P. s. R. o. C. (2012). Design code for heating ventilation and air conditioning of civil buildings (GB 50736-2012 ). In. Beijing, China: China Architecture & Building Press.
Palit, S., Lunniss, P. J., & Scott, S. M. (2012). The physiology of human defecation. Dig Dis Sci, 57(6), 1445-1464. doi:10.1007/s10620-012-2071-1.
Perén, J. I., van Hooff, T., Leite, B. C. C., & Blocken, B. (2015). CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location. Building and Environment, 85, 263-276. doi:10.1016/j.buildenv.2014.12.007.
Regulation, S. A. f. M. (2021). Hygienic specification for public toilet (GB/T 17217-2021). In. Beijing, China: Standards Press of China.
Sato, H., Hirose, T., kimura, T., Moriyama, Y., & Nakashima, Y. (2001). Analysis of Malodorous Volatile Substances of Human Waste: Feces and Urine. Journal of Health Science, 47(5), 483-490.
Seo, Y., & Seouk Park, I. (2013). Study for flow and mass transfer in toilet bowl by using toilet seat adopting odor/bacteria suction feature. Building and Environment, 67, 46-55. doi:10.1016/j.buildenv.2013.05.001.
SeppȨnen, O. (2008). Ventilation strategies for good indoor air quality and energy efficiency. International Journal of Ventilation, 6(4), 297-306.
Sinha, K., Yadav, M. S., Verma, U., Murallidharan, J. S., & Kumar, V. (2021). Effect of recirculation zones on the ventilation of a public washroom. Physics of Fluids, 33(11).
Tung, Y.-C., Hu, S.-C., & Tsai, T.-Y. (2009). Influence of bathroom ventilation rates and toilet location on odor removal. Building and Environment, 44(9), 1810-1817. doi:10.1016/j.buildenv.2008.12.005.
Wang, J.-X., Wu, Z., Wang, H., Zhong, M., Mao, Y., Li, Y., . . . Yao, S. (2022). Ventilation reconstruction in bathrooms for restraining hazardous plume: Mitigate COVID-19 and beyond. Journal of Hazardous Materials, 439, 129697. doi:https://doi.org/10.1016/j.jhazmat.2022.129697.
Yang, C., Yang, X., Xu, T., Sun, L., & Gong, W. (2009). Optimization of bathroom ventilation design for an ISO Class 5 clean ward. Building Simulation, 2(2), 133-142. doi:10.1007/s12273-009-9310-1.
Yang, J.-H., & Kim, O. (2016). Improvement of ventilation efficiency by changing the shape of glass partition in bathroom of apartment house. Indoor and Built Environment, 26(9), 1274-1291. doi:10.1177/1420326x16641313.
Ye, W., Zhang, X., Gao, J., Cao, G., Zhou, X., & Su, X. (2017). Indoor air pollutants, ventilation rate determinants and potential control strategies in Chinese dwellings: A literature review. Science of The Total Environment, 586, 696-729. doi:10.1016/j.scitotenv.2017.02.047.
Yongan, A., Li, W., Xin, J., & Chao, G. (2011). Numerical Simulation of Pollutant Diffusion Law in Bathroom and Optimization of the Locations and Ways of Exhaust Outlet and Makeup Air. Journal of Shenyang Jianzhu University ( Natural Science), 27(4), 720-724.
Zhang, T., Su, Z., Wang, J., & Wang, S. (2018). Ventilation, indoor particle filtration, and energy consumption of an apartment in northern China. Building and Environment, 143, 280-292. doi:10.1016/j.buildenv.2018.07.020.
Zhang, Z., Zeng, L., Shi, H., Liu, H., Yin, W., Gao, J., . . . Zhou, X. (2022). CFD studies on the spread of ammonia and hydrogen sulfide pollutants in a public toilet under personalized ventilation. Journal of Building Engineering, 46, 103728. doi:https://doi.org/10.1016/j.jobe.2021.103728.
Zhang, Z., Zeng, L., Shi, H., Liu, H., Yin, W., Shen, H., . . . Zhou, X. (2021). CFD Study on the Ventilation Effectiveness in a Public Toilet under Three Ventilation Methods. Energies, 14(24), 8379. doi:10.3390/en14248379.
Zhang, Z., Zeng, L., Shi, H., Yang, G., Yu, Z., Yin, W., . . . Zhou, X. (2021). Dynamics and Numerical Simulation of Contaminant Diffusion for a Non-Flushing Ecological Toilet. Energies, 14(22). doi:10.3390/en14227570.
Zhao, L., Liu, J., & Ren, J. (2018). Impact of various ventilation modes on IAQ and energy consumption in Chinese dwellings: First long-term monitoring study in Tianjin, China. Building and Environment, 143, 99-106. doi:10.1016/j.buildenv.2018.06.057.
Zhuang, R., Li, X., & Tu, J. (2013). CFD study of the effects of furniture layout on indoor air quality under typical office ventilation schemes. Building Simulation, 7(3), 263-275. doi:10.1007/s12273-013-0144-5

No competing interests reported.

Download PDF

Editor invited by journal
12 Aug, 2024
Submission checks completed at journal
08 Aug, 2024
First submitted to journal
01 Aug, 2024

You are reading this latest preprint version

Optimization of Toilet Bowl Ventilation technology for odor control and energy efficiency enhancement in public toilet

Status:

Version 1

Abstract

Figures

1 Introduction

2 Methodology

2.1 Determine odor indicator parameters

2.2 Optimized design of toilet bowl ventilation

2.3 Geometric modeling and meshing

2.4 Boundary conditions and working cases

2.5 Experimental verification

3 Results and Discussion

3.1 Simulation results for different airflow

3.2 Simulation results for different odors

3.3 Quantitative analysis of ventilation performance

3.4 Energy savings analysis

4 Conclusion

Declarations

Author Contribution

Acknowledgement

Data availability

References

Additional Declarations

Status:

Version 1