Improvement of Dust Particle Suction Efficiency by Controlling the Airflow of a Regenerative Air Sweeper

Abstract

In a regenerative air sweeper, airflow and dust particles entering the system are filtered and recirculated within the system. The uncirculated portion of the exhaust air in the system spreads to the ambient air, and PM_2.5 dust in the air can poison the environment and adversely affect human health. The development of an airflow control system to reduce road dust emissions and improve air quality was the main contribution of this study. A regenerative air sweeper airflow control system is designed to direct the air from the centrifugal fan back into the pickup head to fully absorb the dust particles and balance the positive and negative air pressures inside the pickup head. The modeling and analysis of the dust control system were performed using an experimental test rig system. A mathematical model of the fundamental parameters of the regenerative air sweeper and dust control system was established. Computational fluid dynamics (CFD) ANSYS was used for the analysis to determine the direction of airflow via the suction and inlet ducts. The discrete particle model (DPM) accurately predicted particle trajectories and measured the suction efficiency of particles of different shapes and types. By controlling the circulating harmful air flow in the system, the amount of PM_2.5 released into the atmosphere was reduced by 90%. The suction efficiency of the 200 $\mathsf{\mu }\mathrm{m}$ sized sand particles was higher than 95%. The results provide theoretical and methodological assistance for the development of improved road sweeper dust control systems.

1. Introduction

Incomplete removal of dust particles from the streets can lead to unpleasant odors due to hot weather or under the influence of strong winds, dust, and particles spread throughout the street, leading to pollution in the city. Timely and effective cleaning of dust and debris on city roads can prevent human health and environmental pollution [1,2,3]. Regenerative air road cleaning vehicles are designed to remove dust particles from the road surface, which ensures that the polluted airflow is recirculated in the system along with the entry of dust particles into the system through the suction mouth. The regenerative air sweeper also has minor drawbacks. The circulating airflow in this system may change the direction of the airflow during collision with the road surface, and the airflow may not be fully reabsorbed in the system. Failure to recirculate the airflow in the complete system causes it to spread to the environment.

It is very important that street cleaners have high efficiency in absorbing dust and particles. For example, Cai et al. [4] analyzed the mechanical behavior of dust particles and the relationship between the pressure distribution and dust particle velocity and obtained optimal performance parameters. According to Jin et al. [5], increasing the width and outlet diameter of the pickup head affects the flow-field characteristics and dust suction efficiency. However, an increase in driving speed increases the relative speed between the dust particles and the inlet surface and causes dust particles to spill. By improving the design of the pickup, Wu et al. [6] found that the pressure drops across the pickup head and the inclination angle of the rear edge wall have a significant impact on particle removal performance. They recommended the need to consider designing a pickup head with a high removal efficiency and low pressure drop. In a simulation study by Xi et al. [7], the efficiency of particle assimilation was increased by changing the size of the suction mouth of the dust particles. Bai and Jin et al. [3,8] found that changing the outlet diameter and angle of the dust suction port resulted in a change in both the removal efficiency and flow field characteristics with an increase in the angle. The optimum angle was 65° and the outlet diameter was 160 mm. Jamshid et al. [9] studied the factors affecting the particle suction efficiency. Higher efficiencies were achieved at higher sweeper speeds and higher particle densities by increasing the airflow rate at the suction port. At a sweep speed of 6–10 km/h, the results showed that the secondary air circulation changed from 60 to 80%, the high-pressure drop was from 2200 to 2400 Pa, and the recorded particle absorption efficiency was 95%.

The structure and characteristics of the particles significantly affect the efficiency of dust particle suction in regenerative sweepers. Pickup velocity is another important parameter of interest in the design and performance of the pickup head. Particle entrainment was investigated by measuring the velocity required at the pickup head [10]. The dominant forces and particle size were the contributing factors that determined the magnitude of the pickup velocity. For example, larger particles demand a greater velocity at the pickup head, and vice versa. The critical pickup velocities of particles were experimentally studied by Kalman et al. [11] to evaluate the performance of the pickup head for varying particle shapes, sizes, and densities. It was observed that for large particles, the pickup velocity increased as the diameter increased, owing to the increase in gravitational force. Furthermore, Ramadan et al. [12] provided a mechanical model that predicted the critical velocity required for the initial velocity of the particles in the solid layer. Ekanayake et al. [13] investigated the lift and drag forces acting on a spherical particle in a flow field bounded by a single wall, using numerical computations.

Additionally, Hu et al. [14] presented methods to improve dust removal performance by experimentally studying the effects of changes in dust composition on cleaning efficiency. Overall, the total dust and respirable dust removal efficiency were 96.81% and 95.59%, respectively.

In recent years, the application of simulation and parametric analysis has improved the design structure and performance of pickup heads. The optimal solution can be achieved through simulation by reviewing the geometric design and the effects of parameters such as the width, outlet diameter, inlet pressure, and airflow velocity of road sweepers [5]. Further improvement can be achieved by analyzing the airflow movement, thereby changing the structure of the system and mechanisms [15,16]. The particle removal performance of the pickup head was numerically investigated [17] for a street vacuum sweeper. An integrated numerical model to predict the flow of gas–solid particles was studied using the Reynolds stress model (RSM) and discrete particle model (DPM) using computational fluid dynamics (CFD) software. The numerical results indicate that the pressure drop and sweeper traveling speed across the pickup head significantly affect the particle removal performance. Using the Euler–Lagrange multiphase computational fluid dynamics (CFD) model, Han et al. [18] found that the airflow in the actual working environment, air pressure, speed, and flow rate added an external air domain outside the port. Xi et al. [19,20] studied the influence of variable operational conditions and structural parameters on particle separation performance. Moreover, the effect of changes in particle velocity and trajectory on collection efficiency was also realized by CFD modeling. The results showed that changes in particle structure, particle velocity, and pressure in the assimilation mouth appear to affect the efficiency of dust particle collection.

The explosion propagation and prevalence of dust explosions in a typical dry dust collector connected to dust collection pipes were investigated using CFD [21] to limit the spread of dust pollution into the environment. Hu et al. [22] presented a numerical simulation method to study dust dispersion characteristics and dust control measures during continuous dusting and dusting periods. Liu et al. [23] studied fugitive dust control technologies for agricultural harvester machinery and presented four fugitive dust emission reduction technologies and five particle measurement methods. Chen et al. [24] explored the dust removal performance of a newly structured microporous membrane filter plate through experiments. Nie et al. [25] analyzed dust control effects under different dust generation conditions using numerical simulations. Based on a parametric analysis, Zhang et al. [26] presented the importance of evaluating the assimilation port simulation results with different structures when calculating the limit values of the geometric parameters. The flow simulation results for the dust suction mouth provide practical guidance for flow simulations. By monitoring the cleaning system, it was found that the location of the waste on the road surface and the distance between the suction nozzle and particles are important in the process of collecting particles and debris [27,28]. A sweeping centrifugal fan creates a pressure difference in the collection of debris and dust on the road, which allows the removal of debris and dust from the road [29,30]. Research on technology for the visual cleaning of road waste based on the intelligent control of road sweepers has shown that it is very effective in waste collection [31,32]. The fact that many sweepers are only engaged in garbage collection on the road surface and the movement of particles is not controlled usually affects the assimilation efficiency of particle movement [33,34]. In a regenerative air sweeper, it is important to control the movement of particles because the airflow out of the system is recirculated through the pickup head inlet duct; if the airflow is fully controlled, it can be ensured that the particles in the recycled air stream do not disperse into the environment under different road conditions and at different driving speeds.

This study proposes a secondary airflow control system for regenerative sweepers to improve particle suction efficiency by controlling the recirculated airflow in the system and reducing the amount of PM₂.₅-sized particles released into the environment. ANSYS computational fluid dynamics (CFD) was implemented in the pickup head inlet and suction port to accurately assess the circulation of toxic secondary air streams moving at different speeds. Using ANSYS FLUENT, discrete particle models (DPMs) can be used to predict the particle trajectory and measure the particle assimilation efficiency, and then the results are compared with experimental data.

The overall structure of this paper takes the form of six parts, including Section 1 that deals with the background, research gaps, and aims of the study. Section 2 begins by laying out the theoretical dimensions of the research by focusing on the dust control mechanism. Section 3 concerns the mathematical model used. Section 4 presents the experimental procedure and the verification of the study. Section 5 presents the results and discussion of this study, and Section 6 gives the conclusion to this study.

2. Basic Theory of the Dust Control Mechanism of the Regenerative Air Sweeper

The function of the regenerative air vacuum cleaner is to ensure that the secondary polluted airflow does not spread to the environment by redirecting it to the system. Under different driving conditions, it may be difficult for the base road cleaner to absorb all the particles because of the negative pressure surrounding the suction port when the particle composition changes. The secondary airflow in the system causes the particles and secondary airflow to not be fully absorbed at the base because of the change in distance between the suction port and road surface. It is sometimes impossible to ensure complete circulation of the secondary polluted airflow in the system when the airflow changes its trajectory owing to collision with the road surface. Controlling the airflow in a regenerative air vacuum sweeper can reduce the amount of secondary particulate air expelled into the environment. A schematic of the regenerative air vacuum road cleaning dust control mechanism is shown in Figure 1. A frequency converter (SQ580-011G) was used to control the airflow through a centrifugal fan. This allows the system to modify the airflow. The pickup head suction port suctioned dust particles into a hopper, which was emptied by the suction duct, and the airflow was moved to the centrifugal fan through the filter. A centrifugal fan outlet duct directs the airflow to the divider, where the airflow can be diverted in two directions. In the airflow separator outlet conduit, a filter was installed and cleaned prior to the release of PM₂.₅ dust particles into the environment. A control valve for electronic airflow was used to control the amount of air released into the atmosphere and the amount of air that could be recycled in the system to achieve greater suction efficiency.

The airflow section of the pickup head was controlled by a mechanical valve installed in the ductwork of the outlet section for even distribution or modification of the airflow entering the inlet ducts. To analyze the movement of air around the pickup head, air velocity sensors were installed to measure the direction and speed of the airflow exiting the system and moving toward the suction port. The suction duct and dust collector hopper contain sensors that monitor air pressure. It is imperative to maintain negative air pressure around the suction tube to increase the effectiveness of dust removal. The data acquisition system monitors the performance of the devices while reanalyzing and storing data. A dust feeder controls the movement of dust particles, and the number of particles is controlled by a control mechanism that allows complete control over the amount of dust particles. The conveyor belt is responsible for moving the dust particles. Using a speed control device, it is possible to increase or decrease the speed of the conveyor belt, which allows the effect of speed change on the cleaning efficiency to be determined.

Physical Model and Mesh of Pickup Head

Figure 2 shows the geometrical parameters of the particle pickup head. In this geometry, the suction of particles is carried out through port D1, in which the blowing inlets D2 and D3 are designed for the movement of the secondary airflow, B is the width of the pickup head, L is the length, H is the height of the front of the pickup head, h is the height of the back of the pickup head, and K is the width of the top of the pickup head, S1 is the height of the suction port, S2 is the height of the blowing inlets, and $\alpha$ is the inclination angle of the suction port.

Table 1. Technical parameter of the pickup head regenerative air vacuum road cleaner.

Name	Unit	Value
Pickup head length	L (mm)	1000
Pickup head width	B (mm)	350
Pickup head front height	H (mm)	130
Pickup head back height	k (mm)	50
Pickup head top width	K (mm)	125
Inclination angle of the suction port	α (°)	75
Suction port diameter	D1 (mm)	110
Inlet diameter	D2 (mm)	65
Inlet diameter	D3 (mm)	65
Suction port height	S1 (mm)	160
Blowing inlets height	S2 (mm)	165

shows the parameter values of the pickup head model.

The pickup head mesh consists of elements that contain nodes (coordinate spaces in space that may vary depending on the type of element) that represent the shape of the grid geometry. The quality of these shapes can be analyzed using indicators such as curvature and aspect ratio. A mesh schematic of the pickup head simulation model is shown in Figure 3. The coupling holder was used to study mesh cleaning by additional analysis of the connecting rods in ANSYS FLUENT using meshes of different densities. The main parameter settings of the simulation are show in

Table 2. Simulation parameters.

Parameter	Units	Value
Particle model		Rosin–Rammler
Total flow rate	$q_m$$(\mathrm{kg}·{\mathrm{s}}^{-1}$)	0.5
Gas density	${\rho }_g$$(\mathrm{kg}·{\mathrm{m}}^{-3}$)	1.225
Particle density	${\rho }_p$$(\mathrm{kg}·{\mathrm{m}}^{-3}$)	Depending on the particle type
Distribution density	${\rho }_d$$(\mathrm{kg}·{\mathrm{m}}^{-2}$)	0.15
Particle mean diameter	$d_m$$(\mathsf{\mu }\mathrm{m}$)	100
Spread parameter	n	5.95
Normal restitution coefficient	$e_{nor}$	0.95
Tangential restitution coefficient	$e_{tang}$	0.85
Near-wall treatment		Scalable wall function
TKE Prandtl number		1
TDR Prandtl number		1.3
Energy Prandtl number		0.85
Wall Prandtl number		0.85
Coefficient	$C_{mu}$	0.89
Coefficient	$C_{1\epsilon }$	1.46
Coefficient	$C_{2\epsilon }$	1.89
Coefficient	${\delta }_{\epsilon }$	1.4

. In the meshing process, a tetrahedral mesh was used for the internal flow field of the dust extraction port, whereas a hexahedral mesh was used for the external air domain. Three mesh numbers, namely, 193,456, 256,712, and 293,786, were selected and fabricated with the same machine under the same operating conditions; that is, the error was the largest when the number of cells was 193,456. The results show that increasing the number of grids does not cause any difference. The calculation results of the other two meshes were almost close to each other. We selected 293,786 mesh as the main mesh grid system in this study.

3. Mathematical Model

3.1. CFD Model Construction

FLUENT is a commercially available CFD software that uses a finite volume formula to perform numerical modeling. The simulation used a regenerative air sweeper dust control system to determine the airflow recirculation in the system together with the calculation of the trajectory of the particles. Determining the change in airflow direction by changing the airflow rate in the particle suction mouth of the pickup head led us to determine the order of movement of the dust particles. The simulation was solved by connecting a solvent in an unstable and stable state using ANSYS FLUENT. For simulations using the RNG k–$\epsilon$ model, a steady-state resolver is sufficient to achieve convergence. In the simulation process, the $Y_d$ distribution of the volume of several types of particles by mass was determined using the Rosin–Rammler distribution.

$$Y_d=exp\left[-{\left({\rho }_{\rho }/d_{\rho }\right)}^n\right]$$

(1)

where ${\rho }_p$ and $d_p$ are the density and diameter of the particles, respectively, and n is the spread parameter.

3.2. Equations for the Flow Field

The continuity equation:

$$\frac{\partial }{\partial t}+\nabla ·\left(\rho U\right)=0$$

(2)

where $\rho$ is the air density, and $U$ is the air velocity.

The mass conservation equation:

$$\frac{\partial u_i}{\partial {\chi }_i}=0$$

(3)

The momentum conservation equation [4,8]:

$$\frac{\partial }{\partial {\chi }_i}\left(\rho u_i{u}_j\right)=-\frac{\partial p}{\partial {\chi }_i}+\frac{\partial }{\partial {\chi }_j}\left(\mu \frac{\partial u_i}{\partial {\chi }_j}+{\tau }_{ij}\right)+\rho g_i$$

(4)

where $u_i$ is the time-averaged air velocity, ${\chi }_i$ is the Cartesian coordinate component, $\rho$ represents the density, ${\tau }_{ij}=-\rho u_i^{\prime }{u}_j^{\prime }$ is the Reynolds stress, p is the air pressure, and $g$ represents the acceleration of gravity.

The turbulence energy transfer equation is given by [9]

$$\rho \frac{dk}{dt}=\frac{\partial }{{\partial }_{xj}}\left[\left(\mu +\frac{{\epsilon }_m}{{\sigma }_k}\right)\frac{\partial k}{\partial xj}\right]+G_k-\rho \epsilon$$

(5)

The turbulent flow energy dissipation rate transmission equation is [9,19]

$$\rho \frac{d\epsilon }{dt}=\frac{\partial }{{\partial }_{xj}}\left[\left(\mu +\frac{{\epsilon }_m}{{\sigma }_{\epsilon }}\right)\frac{\partial k}{\partial xj}\right]+C_{1\epsilon }\frac{\epsilon }{k}{G}_k-C_{2\epsilon }\rho \frac{{\epsilon }^2}{k}$$

(6)

where $G_k=-\rho u_i^{\prime }{u}_j^{\prime }\frac{\partial u_i}{\partial xj}$ is the turbulent kinetic energy production, k is the turbulent kinetic energy, ε is the turbulent kinetic energy dissipation, $\mu$ is the viscosity coefficient of laminar flow, $p_0$ represents the static pressure, ${\sigma }_k=1$ and ${\sigma }_{\epsilon }=1.3$ represent the turbulent Prandtl numbers of k and ε, respectively, and $C_{1\epsilon }=1.44$ and $C_{2\epsilon }=1.92$ are constants.

3.3. Particle Movement

Typically, the Euler–Lagrange method is used to predict gas flow in the suction port area of the pickup head. It is expedient to consider the gas phase as a continuum by solving the Reynolds-averaged Navier–Stokes equations described above, whereas the solid phase was calculated by observing the particles through a constant liquid field. In the process of absorbing dust particles, the solid phase is present in a low-volume fraction; therefore, the gas–solid flow is assumed to be a liquefied phase flow. The discrete phase model (DPM) calculates the trajectory of a particular particle by integrating the force balance in the particle. The equilibrium equation of the forces based on Newton’s second law can be described as follows:

$$m_p\frac{d{U}_p}{dt}=F_D+F_g+F_s$$

(7)

The trajectory of motion of an individual particle can be realized by integrating the particle force balance, where $m_p$ is the particle mass, $U_p$ is the velocity of the particle, $F_d$ is the drag force, $F_{g }$ is the gravitational force, and $F_s$ is the lift force.

The drag force can be written in terms of the drag coefficient $C_D$ as

$$F_D=\frac{18\mu }{{\rho }_p{d}_p^2}\frac{C_{D}R{e}_r}{24}{m}_p\left(U-U_p\right)$$

(8)

where the drag coefficient can be obtained from [35,36,37]

$$C_D={(2.25R{e}^{-0.31}+0.36R{e}^{0.06})}^{3.45}$$

(9)

The deposition force $F_g$ generated by gravity can be expressed as [38]

$$F_g=\frac{\pi }{6}{d}^3\left({\rho }_d-{\rho }_{\alpha }\right)g$$

(10)

where d is the particle diameter, ${\rho }_d$ is the particle density (kg/m³), ${\rho }_{\alpha }$ is the gas density (kg/${\mathrm{m}}^3$), and $g$ is the gravitational acceleration (m/${\mathrm{s}}^2$).

Particle lifting force $F_s$ can be written as [17,19]

$$F_s=\frac{2{\rm K}{\nu }^{0.5}\rho d_{ij}}{{\rho }_p{d}_p{(d_{lk}{d}_{kl})}^{0.25}}\left(U-U_p\right)$$

(11)

where ${\rho }_p$ and $d_p$ are the density and diameter of the particle, respectively, $U$ is the air velocity, $v$ is the kinematic velocity, k is the turbulence kinetic energy, $d_{ij}$ is the deformation tensor, and the constant is K = 2.594 [39,40].

$R{e}_p$ is the particle Reynolds number based on the relative velocity and is defined by the following equation:

$$R{e}_p=\frac{\rho d_p\left(U_p-U\right)}{\mu }$$

(12)

where $U_{\rho }$ is the particle velocity, $m_{\rho }$ is the particle mass, $p_p$ is the particle density, and $d_p$ is particle diameter.

3.4. The Centrifugal Fan Load Characteristic

According to the fan similarity law, it can be obtained that the torque of the fan is proportional to the square of the rotational speed. This can be expressed as the following formula:

$$M_{cf}=C{n}^2+M_m$$

(13)

where $M_{cf}$ is the torque of the fan (N$·\mathrm{m}$), C is the fan torque coefficient, $M_m$ is the friction torque (N$·\mathrm{m}$), and n is the rotational speed of the centrifugal fan (rpm).

In the process of speed change, the relationship between the arbitrary rotation speed and the power under the speed is described as follows:

$$N_{rs}={\left(\frac{n_{rs}}{n_{rp}}\right)}^3{N}_{rp}$$

(14)

where $N_{rs}$ is the corresponding power at the rotational speed (kW), $R_{rp}$ is the rated power (kW), $n_{rs}$ is the arbitrary rotational speed (r/min), and $n_{rp}$ is the rated rotational speed (r/min).

The torque under arbitrary rotation is described as follows:

$$M_{ct}=\frac{N_{rs}}{2\pi n_{rp}}=\frac{n_{rs}^2}{2\pi n_{rp}^2}{N}_{rp}$$

(15)

where $M_{ct}$ is the centrifugal fan torque ($\mathrm{N}·\mathrm{m})$.

3.5. Performance Analysis Centrifugal Fan

The efficiency of the centrifugal fan is defined as

$${\eta }_{fan}=\frac{{\rho }_{t}Q}{W}$$

(16)

where ${\rho }_t$, Q, and W are the total pressure increase, volume flow rate, and shaft power, respectively. The total pressure rise (${\rho }_t$) is defined as the total pressure difference between the inlet and exit of the fan.

Flow coefficient is defined as

$$\phi =\frac{Q}{\frac{\pi d_2^2}{4}{u}_2}$$

(17)

where $d_2$ is the impeller outlet diameter, and $u_2$ is the circumferential velocity at the outlet of the motor.

Total pressure coefficient is defined as

$${\psi }_t=\frac{{\rho }_t}{\frac{1}{2}\rho u_2^2}$$

(18)

The particle structure is always important in the motion of the particles. Changes in the particle structure have a significant effect on the flow of these particles and the cleaning efficiency. The Cabrejos model is used for the incipient motion of a single particle initially at rest on the bottom of a horizontal tube and subjected to a steady fully developed turbulent airflow [41]. They assumed that a single particle began to move when the force in the horizontal direction was zero (sliding). The dust starting velocity can be defined as the minimum air velocity at which the dust begins to slide and roll. In other words, the air velocity must be greater than the initial velocity so that the dust particles are likely to move [42].

$$U_{ps}=\left[1-{\left(\frac{d}{D}\right)}^{1.5}\right]\sqrt{\frac{4fgd}{3C_k}\left(\frac{{\rho }_c-\rho }{\rho }\right)}$$

(19)

where $U_{ps}$ is the dust-starting critical velocity (m/s), ${\rho }_c$ is the density of dust (kg/${\mathrm{m}}^3$), $\rho$ is the density of airflow (m/s), $g$ is the gravitational acceleration (m/${\mathrm{s}}^2$), d is the diameter of dust (mm), $C_k$ is the start coefficient, and f is the friction coefficient of the particles and flow tube.

Assuming that the particles are spherical, the particle size is within 3~100 µm, and in line with Stokes Law, the viscosity resistance F for separating the particles from the dusty air is

$$F=3\pi \mu d{\nu }_g$$

(20)

where F is the gas resistance (Pa), µ is the gas viscosity (Pa$·$s), d is the particle diameter (mm), and ${\nu }_g$ is the separation velocity (m/s).

In regenerative air cleaners, secondary particulate airflow recirculation occurs in the system. It is very difficult to ensure the complete circulation of the secondary airflow in the system, and the change in sweeper speed with a negative pressure around the suction port results in the secondary airflow in the system not circulating completely and spreading to the environment in the pickup head chamber. To reduce the negative air pressure and air resistance around the particle suction port, it is necessary to study the effect of the recirculated secondary airflow in the system on the suction efficiency. The secondary airflow rate recirculated from the pickup head inlet ducts is given by the following equation:

$$V_{psac}=\frac{(V_{full.air}-V_{atm})}{100}·100\%$$

(21)

where $V_{full.air}$ is the total secondary airflow rate in the system, and $V_{atm}$ is the secondary airflow rate released into the atmosphere through filtration.

3.6. Boundary Conditions and Solution Controls

The boundary conditions consisted of two inlets for the return airflow from the system, an inlet duct for atmospheric airflow with particle movement, an outlet duct for the particle intake orifice, and walls of the collection head. The average value of the circulating airflow in the system was 15 m/s for inlet ducts D2 and D3 of the pickup head. For the atmospheric airflow and particle movement, the airflow velocity at the inlet was 35 m/s. The expansion surfaces were exposed to the atmosphere; therefore, the value of the inlet pressure was 0. Thus, the inlet return airflow from the system was set to the pressure inlet, and the inlet static pressure was 0 Pa. The pickup head suction port was set to the pressure outlet, and the exhaust pipe outlet and centrifugal fan were connected according to the actual pressure measurement of the dust hopper. The wall surface at the bottom of the pickup head was a nonslip stationary wall. When the “reflect” condition was applied to each wall to set the boundary conditions, it was assumed that the particles with the “escape” condition would be exported to the remaining inlet and outlet ducts. For the k–$\epsilon$ model, a feasible standard wall function was assumed. For the calculation, a steady-state solution method was assumed. The solution methods, a pair of pressure–velocity, Green–Gaussian cells based on gradient discretization, and second-order upwind of momentum were assumed [7,17,20].

The boundary conditions in the airflow and the top and bottom of the gridding region were selected as the specific velocity, inlet flow, outflow, and no-slip wall conditions, respectively (Figure 4).

The quality of these shapes can be analyzed using indicators such as curvature and aspect ratio. We used the SIMPLEC method for the pressure–velocity coupling because of the complicated airflow. The effects of particle dispersion were simulated using a discrete random walk model. As a result, the under-relaxation factors were set to 0.2–0.5, to allow for easy convergence. The convergence criteria consisted of checking residuals and monitoring the integrals of the inlet and outlet pressure surfaces. The convergence factors of the energy equation were set to $1\times {10}^{-8}$, and those of the other variables were set to 1 $\times {10}^{-5}$. Jet sources were used to remove particles from the inlet of the particle suction ports, and the boundary positions were set for escape particles. There is a boundary condition inside the assimilation port and a trap on the output boundary. A particle was trapped at the exhaust duct outlet during the simulation, and the particle tracking stopped. The following methods were used to evaluate the effectiveness of particle removal. The particle suction efficiency is calculated by counting the number of particles trapped (trap) at the exit and the number that escape (escapes) [5,8].

In the first order of determining the efficiency of assimilation of particles, we use the following equation:

$${\eta }_1=\frac{trap}{tracked}$$

(22)

where ${\eta }_1$ is the particle suction efficiency and track is the total number of particles incident.

According to Wu et al. [17], the total removal efficiency represents the ratio of the particles removed to the particles injected into the pickup head, which can be calculated based on the input and output of the total mass flow. The overall removal efficiency was determined using the following equation:

$${\eta }_2=\frac{G_1}{G_2}·100\%$$

(23)

where ${\eta }_2$ is the overall removal efficiency, $G_1$ is the inlet mass flow rate (kg/s), and $G_2$ is the mass flow rate (also known as the mass flow rate) (kg/s).

To determine the efficiency of particle separation from the surface during an experiment, it was necessary to determine the weight of the particles. In this experiment, a special sieve was used to measure the size and quantity of the particles, allowing us to accurately determine the size of the particles. Once the particle size was determined, a scale was used to measure the mass of the particles. The mass of the particles can be used to measure the efficiency of the particles on a road surface. The ratio of the number and mass of particles on the road surface of the collected particles determines the suction efficiency of the particles in the experimental results. The removal efficiency was calculated using the following equation:

$${\eta }_{ex}=\frac{P_m}{P_m+P_{rm}}·100\%$$

(24)

where ${\eta }_{ex}$ is the efficiency of particle collection on the road surface, $P_m$ is the mass of the total particles on the road surface, and $P_{rm}$ is the mass of the residual particles after collection from the road surface (kg).

A comparison of the suction efficiency of the experimentally determined particles with the simulation results is performed in the following order: to ensure that the size and type of particles are homogeneous, the airflow rate moving through the inlet and outlet ducts must have the same value as in the experiment. We can then compare the results obtained in the experiment with the simulation results.

3.7. Model Validation

Model verification is an important procedure for ensuring the accuracy and reliability of numerical simulation results. To increase the reliability of the suction efficiency of road cleaners, road cleaners, two experimental validations were performed. The application of the pickup head geometric model was first confirmed by the factors affecting the efficiency of particle suction. The second verification was to verify the accuracy of the simulations of the absorption efficiency of particles by a pickup head.

In the validation, the data used in the present study to evaluate the CFD model were obtained from Wu et al. [17]. Based on the efficiency of the sand particles used in the pickup head suction port for use in the experiment, and in the numerical simulation verification, we were able to determine the effect of the observed difference in the sweeper traveling speed on the suction efficiency. The sweeping field experiments were carried out at a sweeper traveling speed of 6–14 km/h, pressure drop of 2000 Pa, sand particle sizes from 45 to 160 $\mathsf{\mu }\mathrm{m}$, density of 2500 kg/${\mathrm{m}}^3$, and the direction of movement of the sweeper was Y = 900 mm in the plane Z = 20 mm. The size of the experimental road surface at 2.0 $\times$ 1.8 mm. A road sweeper pickup head was used as the road cleaning device.

In the second validation, the concentration obtained by the DPM was compared with those in the experiments conducted by Xi et al. [19]. The overall absorption efficiency of the particles was assessed by conducting sweeper traveling speeds of 5–14 km/h, pressure drops of 1400–2900 Pa, particle sizes of 45–152 $\mathsf{\mu }$m, and reverse-blowing flow rates of 1227–3120 ${\mathrm{m}}^3$/h.

Based on the two experiments, a geometric model and consistent boundary conditions were established. The simulation data agreed well with the experimental data. When the traveling speed was 6–16 km/h, the average deviation between the simulation and experimental results was less than 10%. However, when the sweeper speed was 12–16 km/h, the simulated values were slightly higher than the experimental ones. There may be a reason for the sufficient accuracy of the DPM model in simulating the sweeper pickup head particle suction efficiency. As for the concentration, the numerical simulation results are normalized, and the average concentration at speeds of 6 to 10 km/h is very consistent with the experimental data. However, there is a small deviation from the normalized average concentration at a sweeper speed of 6–10 km/h, which may be due to the difference in the airflow. Despite some slight differences, the realizable k–ε turbulence model is feasible for solving fluid flow and solid particles.

4. Experiment Verification

The experimental setup of the regenerative air road sweeper dust control system is shown in Figure 5. The prototype of the pickup head, particle suction port, and structure of the inlet ducts providing the secondary circulation of the airflow in the system were designed based on the calculation and experimental results. The function of the airflow splitter in the system is to direct a portion of the secondary airflow to the pickup head inlet ducts for recirculation in the system and to distribute the remainder to the environment.

The experimental phase consists of the following:

• The centrifugal fan (CY200H) with a rotation speed of 2900 r/min was regulated by a frequency converter (SQ580-011G/015P4) to produce an airflow velocity of the system 5–35 m/s and create a pressure drop of 1200 to 2400 Pa.
• The conveyor belt speed was regulated by the control panel (ADLEEPOWER AS2-107) to produce the 4–16 km/h.
• The dust feeder equipment was developed considering the uniform distribution of dust particles during conveyor belt movement. This device was controlled by a speed controller motor (US-52) and had a speed of 90–1400 r/min.
• The flowmeter (Longlv LL-DC DN100PTFE) instrument was used to measure the flow rate at each experimental point in the system.
• An air pressure sensor (PTL 516) was used to measure the air pressure around the pickup suction port and the air pressure in the particle dust collector hopper.
• An air control valve (Q911F-10S) was used to ensure that the secondary airflow was recirculated in the system. It was possible to change the airflow from the electronic control valve to the environment by 0–100%.
• The particle counter (MKS800) used to measure the number of particles released into the atmosphere allowed observation and analysis of the number of particles in the secondary air.
• The mass of the particles used in the test was measured and recorded. At the end of the experiment, the weight of the particles in the particle receiving hopper and the weight of the nonabsorbed particles were determined, which allowed us to determine the exact amount of suction efficiency.
• The results obtained were reanalyzed and the effectiveness was evaluated.

5. Results and Discussion

5.1. Simulation

It is recommended to use the ANSYS flow field model (CFD) to properly organize the secondary airflow circulation in regenerative air. The pickup head inlet and suction port are important devices for the effective implementation of secondary airflow recirculation in the system. The exact assessment of the velocity and trajectory of the airflow moving through the pickup head inlet and suction port is shown in Figure 6. As we know that the sweepers move at a certain speed during the road cleaning process, the secondary particle airflow through the pickup head inlet ducts moves towards the suction duct for circulation in the system. The ambient airflow should not exert a negative pressure around the suction port during the addition of recirculated secondary airflow to the system. If the suction port airflow rate is higher than the secondary airflow passing through the inlet ducts, it results in less resistance when the surrounding airflow is joined. In addition, the wind resistance must always be taken into account, which affects the complete circulation of the secondary airflow recirculated in the system as a result of the movement of the sweeper at different speeds during operation.

Figure 7 shows the velocity and trajectory of the particles. Road cleaners are not always highly efficient, because the particles on the road surface have different structures and sizes. Studies have shown that to improve the assimilation efficiency of particles located on the road surface, the airflow rate in the assimilation port must be higher than the velocity of the moving particles. This was used to accurately predict the particle trajectories and particle absorption efficiency of the discrete particle model (DPM). Depending on the type and structure of the particles, the effect on the absorption efficiency of the particles makes it possible to obtain precise data. When we increased the suction port air flow rate to 35 m/s, the suction sand particle efficiency was greater than 0.91 $\eta$, whereas in clay particles, this efficiency was 0.88 $\eta$.

5.2. Particle Removal Efficiency

Factors affect the efficiency of particle assimilation in the secondary airflow circulating in the complete system during operation of the regenerative air sweeper. In regenerative air sweepers, the speed of movement is of great importance for clearing dust particles on city roads. Figure 8 shows the effect of changes in the conveyor belt speed on the particle suction efficiency. The measurements and analyses indicated that the computational and experimental results were relatively close to each other. The suction efficiency of the particles was high at a conveyor belt speed of 6–10 km/h. The absorption efficiency of the particles reached a lower value when the movement speed exceeded 12 km/h. The results of the calculations and experiments show that the suction efficiencies of the particles at a speed of 6–8 km/h are very close to each other, and the difference between the results is 1%. The conveyor speed was between 10 and 12 km, and the difference between the simulation and experimental results was 2%. At a speed of 14 km/h, the difference was 5%. When the conveyor belt speed was 16 km/h, the difference between the particle absorption efficiency calculation and experimental results was 8%. Sand particles smaller than $<$200 $\mathsf{\mu }$m were used in the simulation and experimental results. Because the simulation result was higher than the experimental results with increasing speed, the values entered with the FLUENT code did not lead to a sharp decrease in the result, but a relatively faster decrease in the result with an increase in speed in the real working mode during the experiment. An increase in the conveyor belt speed had two effects on the overall particle suction efficiency. On the one hand, the relative velocity between the particles and the moving pickup head increased with the conveyor belt speed. The high speed of the cleaner encouraged the particles to move towards the rear narrow slot at a greater impact angle. The suction port of the pickup head cannot collect more particles smoothly with airflow, and most particles escape from the pickup head rear narrow slot into the environment. A high sweeping speed is less efficient than a slow sweeping speed owing to the reduction in particle reception time on the road surface.

Many factors affect particle suction efficiency, one of which is the airflow rate. Insufficient airflow in the pickup head suction duct is ineffective for moving and collecting particles on the road surface. The dust extraction efficiency will be higher if the suction mouth airflow rate is higher than the particle movement speed for efficient collection of particles on the road surface. Figure 9 shows the effect of airflow rate on particle suction efficiency. Although the airflow rate at 15 m/s calculation result was higher than the experimental results, the experimental results were higher than the calculated results after the airflow rate exceeded 20 m/s. The higher the airflow rate in the suction mouth, the higher the efficiency owing to the movement and absorption of more dust particles on the road surface, leading to an increase in the experimental results with an increase in the airflow rate. When we compared the simulation and experimental results, the differences between the results were very similar. The difference between simulation and experimental results was 2% at airflow velocity of 15 m/s; at 20 m/s, the difference was 1.5%. The airflow velocity was 2% at 35 m/s and the particle suction efficiency was 0.9 $\eta$. In the experimental study, the particle suction efficiency was 0.95 $\eta$. For high particle suction efficiency, the suction port airflow velocity should be higher than the initial velocity of the particles.

The effect of the change in the diameters of the suction head and suction tube on the suction efficiency of the particles is shown in Figure 10. We measured the efficiency of particle suction using three different suction tubes during the experiment when the absorption efficiency of leaf particles smaller than 15 mm in size was determined. When the tube diameter was 90 mm, the sweeping surface area was 3.6 m², the inclination angle was 30°, the conveyor belt speed was 6 km/h, and the suction efficiency was 0.95 $\eta$. The diameter of the tube was 110 mm and the suction efficiency was 0.96 $\eta$, while the efficiency at a tube diameter of 130 mm was greater than 0.98 $\eta$. An increase in the conveyor belt speed and a relative decrease in the particle suction efficiency were observed owing to the increase in residence time. When the conveyor speed was increased to 16 km/h, the particle suction efficiency was 0.84 $\eta$ for a tube diameter of 90 mm. The highest efficiency was achieved with a pipe diameter of 130 mm. During the measurement of particle suction efficiency, it was observed that a tube with a size of 130 mm was more efficient when the suction port airflow rate was 35 m/s.

The inclination angle of the pickup head suction is an important part of a good collection of particles. It was necessary to ensure that the suction mouth was at the optimal inclination angle to ensure that the particles were fully absorbed. Figure 11 shows how the suction mouth of the pickup head affects the absorption efficiency of the particles when the inclination angle is different. The efficiency was 0.74 $\eta$ at 20 m/s when the angle of inclination of the suction mouth was 75$°$, and the efficiency was 0.93 $\eta$ at 35 m/s. The inclination angle of the suction mouth was 0.94 $\eta$ at 60$°$ airflow at 35 m/s, whereas the inclination angle at 30$°$ particle suction efficiency was 0.96 $\eta$. The experimental results showed that the collection head was highly efficient when the inclination angle of the suction mouth was 30$°$.

A change in the velocity of the airflow moving through the suction port causes a change in the air pressure in the suction port. In the pickup head chamber, the secondary airflow collision with the atmospheric airflow affects the pressure change during recirculation in the system. The higher the pressure drop in the pickup head chamber, the higher the dust removal efficiency. An increase in the speed of the airflow through the intake port caused an increase in the pressure drop in the inlet chamber of the system.

The effect of particle removal on the pressure drop was measured, and several experiments were carried out with pressure drop values of 1200–2400 Pa across the pickup head when the conveyer belt speed was 8 km/h [17,19]. Figure 12 shows an increase in the overall removal efficiency with increasing inlet pressure drop. This effect can be expected because as the pressure drop at the pickup head increases, more kinetic energy can be obtained through the connection between the particles and the airflow; therefore, more particles can be obtained by the airflow. The clay particle removal efficiency was 0.68 $\eta$ when the pressure drop was 1200 Pa. Removal efficiency in wood particles was 0.82 $\eta$. When the pressure drop was 2400 Pa, the removal efficiency of wood particles was 0.98 $\eta$, that of removal of sand particles was 0.95 $\eta$, and that of clay particles was less than 0.9$\eta$. For small particles, the grade efficiency decreases slightly with the pressure drop, but it increases significantly for large particles. There is a strong relationship between the velocity slip ratio and inlet pressure drop. Low-pressure drops result in lower velocity slip ratios because the velocity slip ratio is highly dependent on the drag force acting on the particles. Increasing the airflow rate at the pickup head suction port can increase the inlet pressure in the system, thereby increasing the efficiency of particle removal.

5.3. Initial Velocity of the Particles

Usually, the structure in the process of cleaning particles plays a significant role in the weight mass. The airflow in the process of blocking particles must be able to move and lift the particles. Under vacuum conditions for the full removal of particles in the pickup head during road cleaner movement, the suction port allows the particles to be fully absorbed when the airflow rate is higher than the velocity at which the particles move. Figure 13 shows the effect of the change in the particle structure on the initial velocity of the particle. The calculation results are based on Equation (20), and the starting velocity of the particles by calculation was 4.3 m/s for particles with a size of 250 $\mathsf{\mu }$m, whereas during the experiment, the starting velocity of the particles was 5.8 m/s. As the airflow rate increased, the calculation rate of the speed at which the particles moved and the results of the experiment were relatively close to each other. The result of the starting velocity calculation of particles with a size of 2000 $\mathsf{\mu }$m was 12.2 m/s, compared with 12.5 m/s through experiments. The results showed that the difference between the simulation and experimental results with a particle size of 250 $\mathsf{\mu }$m was 25%, and with increasing particle size, the initial particle starting velocity was 5% for 1000 $\mathsf{\mu }$m particles and 2% for 2000 $\mathsf{\mu }$m particles. It can be seen that the experimental results for particle sizes from 250 μm to 750 $\mathsf{\mu }$m were higher than the simulation results. However, when the particle size was greater than 750 $\mathsf{\mu }$m, the difference between the experimental and simulation results was very close. Crushed asphalt particles with a density of 2400 kg/${\mathrm{m}}^3$ were used in the calculations and experiments. In many cases, because the particles on the road surface are very diverse, it is important to study the effect of changes in the particle composition on the initial velocity. The effect of structural changes in the particles during the operation of road cleaners on the initial velocity of the particles is shown in Figure 14. Because the size of the particles on the road surface is not always small, when studying the size of the particles encountered in real conditions, it was found that the initial velocity of high-density particles is relatively high [10,11]. The starting velocity of the removal of 3 mm sized wood particles was 7.4 m/s, while the starting velocity of sand particle removal was 12.3 m/s. The starting removal velocities of 15 mm particles was 29.5 m/s in clay particles, 31.7 m/s in asphalt particles, and 33 m/s, in granite particles, respectively. When we changed the particle size to 30 mm during the calculation, the particle starting velocity of the particles was 20.2 m/s for wood particles and the maximum velocity was 42.3 m/s for granite particles.

5.4. Effect on the Efficiency of the Suction of the Recirculated Secondary Airflow in the System

As the recirculating airflow in the inlet duct of the system moves through the pickup head, the path of movement may change owing to an increase in the airflow velocity. During the erratic movement of the particles around the suction port, a certain part of the flow that passes through the inlet duct of the pickup head collides with the road surface, whereas the rest moves toward the suction mouth. The collision of the particle stream in the system with the particle stream on the road surface results in a change in the trajectory of the particles owing to the effect on the motion. Figure 15 shows the effect of the amount of flow re-entering the system on suction efficiency. The assimilation efficiency of light particles was 0.93 η when 10% of the airflow from the system was directed into the inlet duct of the pickup head, whereas the suction efficiency for metal particles was 0.71 η. The highest particle assimilation efficiency was obtained when 60–70% of the airflow in the system was returned through the inlet ducts of the pickup head. When the airflow in the system was fully recirculated, the particle suction efficiency was relatively low. The results of the experiment showed that the airflow in the system did not recirculate by 100% in any case. A certain amount of airflow is released into the environment. There are many reasons for this: change in the trajectory of airflow moving through the inlet duct owing to collision with the road surface, increase in the speed of movement of the sweeper, and negative pressure around the suction mouth, among other reasons.

The amount of secondary airflow returned to the system to achieve greater efficiency is important for a regenerative air vacuum sweeper. In regenerative air vacuum sweepers, it is usually very difficult to ensure that the full airflow is returned to the system because of the speed of movement of the sweeper and the pressure around the suction port. Airflow does not circulate in the complete system, owing to changes in road conditions and changes in the trajectory of the secondary airflow as a result of a collision with the road surface. Figure 16 shows how the secondary airflow affects the suction efficiency of the particles when they are recirculated in the system. When 70% of the secondary airflow was recirculated in the system, the conveyor belt speed achieved a particle suction efficiency of 0.97 $\eta$ at 4 km/h. It can be observed that the suction efficiency of the particles decreases when the speed of the conveyor belt movement increases. The particle collection efficiency was relatively low when the secondary airflow was fully circulated in the system. The efficiency of particle suction during the period of 100% recirculation of air intake was 0.93 $\eta$ at a conveyor speed of 4 km/h, and the efficiency of particle collection was 0.75 $\eta$ at a speed of 16 km/h. The efficiency of particle reception when the secondary air intake in the system was recirculated by 40% was 0.95 $\eta$ per conveyor belt at 4 km/h, while the efficiency at 16 km/h was 0.78 $\eta$. This means that the complete or partial circulation of the secondary airflow in the system may have a significant effect on the particle acceptance efficiency. Figure 17 shows the effect of the secondary airflow in the pickup head inlet ducts on the efficiency of the individual recirculation in the system. Although the high efficiency of airflow introduced through the inlet duct D2 was achieved when 70% of the airflow was recirculated in the system, the airflow introduced through the two input channels was 14% lower than when it was recirculated in the system. Although the secondary airflow D2 and D3 circulated in the system in the individual state, the efficiency of particle suction differed from each other despite the fact that the airflow has the same function in each inlet duct. When the amount of airflow through the D2 and D3 entry channels was equal, an average difference of 2–3% was observed in the particle absorption efficiency.

5.5. Discussion of the Number of Particles Released into the Atmosphere

For any type of road cleaner, it is important to ensure that dust particles in the secondary air are not released into the atmosphere. In Figure 18, we observe the process of dispersion of PM_2.5 particles into the environment in the secondary dusty air stream in the regenerative air vacuum system in the unfiltered state of the air separation device, when the number of particles released into the environment exceeds 26 when the amount of airflow released into the atmosphere is 10%. The number of particles was 326 when the secondary airflow was completely distributed throughout the environment. This may appear to be the case for more dust particles to be released into the environment from a binary air stream, but this can be achieved by controlling the secondary airflow into the atmosphere as an effective solution. Figure 19 shows the reduction in the number of particles in the secondary air stream when the filter was installed in the airflow separation device. The number of PM_2.5 particles was 159 when the airflow in the system was 100% fully distributed to the environment, and the number of particles in the secondary air was seven when the airflow in the system was 10% distributed to the environment. The number of particles in the secondary air stream, where the secondary airflow is 100% distributed to the environment, can be reduced by 51% compared to the unfiltered air splitter device.

6. Conclusions

This study was conducted to improve the particle suction efficiency while reducing the distribution of secondary particulate airflow into the atmosphere by controlling the airflow in a regenerative air sweeper. The computational simulation results were validated by conducting experiments using a specially developed setup. A turbulence model was used to simulate the airflow in the dust suction port, and physical values such as air velocity and pressure were analyzed. The main conclusions following the experimental findings in this study are summarized below:

• The overall dust collection efficiency decreases as the conveyor belt speed increases. When the speed changed from 6 to 16 km/h, the overall efficiency of dust collection decreased from 96% to 81% in the simulation results, and from 95% to 75% in the experimental results.
• The road sweeper has a high efficiency during its operation at speeds of 6–10 km/h, while the intermediate difference is positive compared to the simulation and experimental results. Furthermore, when the pressure drop increased, dust removal efficiency increased.
• The efficiency of dust cleaning depends significantly on the change in the particle structure. For instance, wood particles increased from 81% to 98%, sand particles increased from 76% to 95%, and clay (wet) particles increased from 69 to 90% when the pressure increased from 1200 to 2400 Pa.
• In addition, the velocity of the airflow through the pickup head suction port is critical for improving the particle removal efficiency. The particle removal efficiency was 93% when the airflow rate through the suction port was increased to 35 m/s.
• Furthermore, the particle structure and density significantly affected the starting particle removal velocity. With a particle size of 0.25 $\mathsf{\mu }$m, a particle starting removal velocity of 4.3 m/s was obtained, and when the particle size was 2000 $\mathsf{\mu }$m, the particle removal velocity was 12.3 m/s, which corroborates earlier findings. Sand particles with a density of 1650 kg/${\mathrm{m}}^3$ and size of 30 mm had a particle starting velocity of 33.5 m/s, whereas the starting removal velocity of granite particles with a size of 30 mm and density of 2600 kg/${\mathrm{m}}^3$ was 42.3 m/s.
• It is worth mentioning that high efficiencies were obtained when the particle suction port for the pickup head was designed at an inclination angle of 30°. By controlling the airflow in the sweepers, the amount of harmful PM₂.₅ particles released into the environment can be minimized by recirculating 40–70% of the secondary airflow in the system.

Future research should consider a regenerative air vacuum sweeper by modeling the secondary airflow control mechanism, including dust suction and dust collection mechanisms, particle filtration mechanisms, and secondary toxic airflow control systems. Hopefully, this type of road cleaner will allow the commercialization and effective cleaning of city roads to reduce the amount of harmful dust that spreads into the environment.