A Review of Dust Movement Laws and Numerical Simulation-Based Dust Suppression Methods in Coal Mines

Abstract

Dust generated during coal mining and transportation poses serious threats to miners’ health, operational safety, and the surrounding environment. However, comprehensive review studies on dust suppression in coal mines remain limited, particularly those integrating dust movement laws with numerical simulation approaches. This review presents a systematic and reproducible analysis of dust control methods in coal mines with a particular focus on numerical simulation. Current research progress and development trends are summarized from three aspects: structural optimization of dust suppression devices, optimization of operating conditions, and ventilation system design. Existing studies indicate that structural improvements mainly concentrate on nozzle geometry, diameter, installation position, and spraying distance, while operating condition optimization primarily involves pressure regulation. Due to the complexity and high cost of full-scale experimental platforms, ventilation system optimization is largely achieved through numerical simulation, supplemented by field measurements. Studies based purely on numerical simulations remain limited in addressing the chemical modification of dust removers; however, with the advancement of molecular dynamics techniques, this area may represent a promising direction for future research.

1. Introduction

China possesses abundant coal resources, with proven reserves of 187.55 billion tons as of 2020, making it the largest coal storage country and producer worldwide. In the same year, national coal production reached 3.84 billion tons, accounting for nearly half of global output. Coal mining generates substantial dust, which severely degrades the working environment and contributes to occupational diseases, particularly pneumoconiosis [1,2]. In China, over 90% of the approximately one million occupational disease cases are pneumoconiosis, with 80% of affected individuals employed in coal mines. The annual number of newly reported occupational pneumoconiosis cases in China from 2017 to 2024 is presented in Figure 1 [3,4].

Figure 1. Number of newly reported occupational pneumoconiosis cases in China from 2017 to 2024.

Dust removal in coal mines dates to the late 19th century. At that time, coal miners faced severe health problems, especially respiratory problems, due to the large amount of coal dust and other particulate matter produced during mining. The earliest coal mine dust removal devices were simple washers and wet precipitators, which used water to trap coal dust and other particulate matter. However, these devices have problems such as low efficiency, high cost, and complex maintenance [4]. With the development of technology, coal mine dust removal equipment has been improved. In the early 20th century, mechanical precipitators appeared, using rotating screens or rollers to capture coal dust and other particulate matter. In the middle of the 20th century, the development of electronic technology promoted the appearance of electrostatic precipitators. Coal mine dust removal technology has matured, including ultrasonic dust precipitators, magnetic dust precipitators, electrostatic dust precipitators, surfactant dust precipitators, and other types of equipment [5].

Many scholars have reviewed the current status of coal mine dust removal research. Xu et al. (2018) and Li et al. (2021) introduced the development of surfactant-assisted coal dust suppression technology, including the chemical structure, type, adsorption mechanism, efficiency evaluation methods, and key influencing factors of surfactants, and proposed several suggestions [6,7]. A diagram illustrating surfactant adsorption on the coal surface is shown in Figure 2. While this review provides rich and detailed content, it focuses solely on one dust removal method.

Figure 2. Diagram of adsorption of surfactant on the coal surface [6].

In contrast, Liu et al. (2021) reviewed the research progress on coal workers’ pneumoconiosis and mine dust control [8]. Although the scope is broad, the content lacks specificity. Zhang et al. (2021) also summarized the development status of Chinese technologies for preventing coal mine dust disasters, covering topics such as pneumoconiosis prevalence, dust hazards, dust dispersion laws, coal dust wetting theory, ventilation dust removal, wet dust suppression, and personal protection measures [5]. However, the discussion of the dust remover was limited.

Considering water-based methods, Zhou and Qin (2021) provided a comprehensive and well-organized review of coal dust suppression technologies based on water media and their influencing factors. Nevertheless, their review did not address ventilation-based or dry dust removal methods [9]. Similarly, Zhang et al. (2022) provided an overview of dust removal methods from physical and chemical perspectives, including multiple dust removal techniques, yet ventilation and dry dust suppression were still not covered [4].

As summarized above, existing review papers exhibit certain limitations, such as focusing on a single method, lacking comprehensive coverage, or omitting important approaches like ventilation and dry dust removal. A detailed summary of these reviews on coal dust suppression research is provided in Table 1.

Table 1. Summary of review of coal dust suppression research.

Authors	Years	The Main Line of Review	Limitation
Xu et al. and Li et al. [6,7]	2018 and 2021	Chemical structure, type, adsorption mechanism, efficiency evaluation method, and key influencing factors of surfactant	Only surfactant-assisted coal dust suppression technology is introduced; no other dust removal methods
Liu et al. [8]	2021	Research progress of coal workers’ pneumoconiosis and mine dust control	The content is a little messy and not specific enough
Zhang et al. [5]	2021	the status of pneumoconiosis, dust hazards, dust dispersion law, coal dust wetting theory, ventilation dust removal, wet dust removal, personal protection, etc.	The introduction of the dust remover is limited
Zhou and Qin [9]	2021	Coal dust suppression technology based on a water medium	It does not cover ventilation dust removal and dry dust removal
Zhang et al. [4]	2022	Physical and chemical methods	Ventilation dust removal and dry dust removal are not covered

With the continuous development of coal mine dust removal technology, numerical simulation has become an efficient method for studying and optimizing the coal mine dust removal process. Complex physical processes, such as flow field, dust movement, and particle trapping in a coal mine dust removal system, can be simulated and analyzed through numerical simulation, thereby providing theoretical guidance and engineering optimization basis for the design and improvement of dust removal technology. Ding et al. (2025) combined numerical and molecular dynamics simulations to investigate the wetting and aggregating behavior of surfactants for coal dust suppression, linking microscopic chemical interactions with macroscopic performance [10]. Wang et al. (2025) employed CFD simulations to analyze and optimize the flow field of an air-assisted spraying device on a tunnel boring machine [11]. In addition, Wang et al. (2025) integrated numerical simulations with laboratory and field tests to evaluate the cumulative dust suppression effects of foam and dust extraction fans at a continuous miner face [12]. The aim of this paper is to review the application of numerical simulation in coal mine dust removal research, to summarize current research results, and to look forward to the direction of future development.

2. Systematic Literature Collection

The most important keywords in this review are ‘coal mine,’ ‘fully mechanized mining face,’ ‘dust removal,’ and ‘dust suppression.’ To attempt a liberal and reproducible review, a systematic literature collection was conducted for coal mine dust removal research. The collected works span from 2016 to the present and consist exclusively of English-language literature. The following scientific index engines and search strategies were employed. The number of publications during 2016–2025 in four scientific index engines is shown in Figure 3.

Figure 3. Number of publications during 2016–2025 in four scientific index engines.

(i) Web of Science: (“coal mine” OR “fully mechanized mining face” OR “mining”) (Topic)) and ((“dust removal” OR “control dust” OR “dust suppression”) (Topic)) AND (publication date: 2016–2025).

(ii) Science Direct: (“coal mine” OR “fully mechanized mining face” OR “mining”) AND (“dust removal” OR “control dust” OR “dust suppression”) AND (publication date: 2016–2025).

(iii) IEEE Xplore: (“Full Text & Metadata”: “coal mine” OR “Full Text & Metadata”: “fully mechanized mining face” OR “Full Text & Metadata”: “mining”) AND (“Full Text & Metadata”: “dust removal” OR “Full Text & Metadata”: “control dust” OR “Full Text & Metadata”: “dust suppression”) AND (publication date: 2016–2025).

(iv) Scopus: (TITLE-ABS-KEY (“coal mine” OR “fully mechanized mining face” OR “mining”) AND TITLE-ABS-KEY (“dust removal” OR “control dust” OR “dust suppression”)) AND (publication date: 2016–2025).

3. Law of Dust Movement

The movement law of coal mine dust is significant for dust control. Many scholars have simulated the diffusion law of coal mine dust using Fluent 12.0 software, and some simulation results have been compared with field measurements. Table 2 summarizes published studies on dust dispersion in coal mines. Mathematical models, dust types, and occasions in the papers are listed.

Table 2. Summary of the papers on dust dispersion in the coal mine.

Authors & Years	Mathematical Models	Object	Occasion and Dust Concentration
Hu et al. (2016) [13]	(Fluent) Eulerian–Lagrange method, Eulerian–Eulerian model, DSMC	Respirable dust, Dust (>5 µm in diameter), Total dust	Blasting heading face; Average dust concentration: 1500 mg/m3
Chen et al. (2018) [14]	(Fluent) Eulerian method, Eulerian–Eulerian method, Eulerian–Lagrangian method	Total dust	Fully mechanized mining face with a mining height of 7 m; Dust concentration: 2850 mg/m3 and 1700 mg/m3
Zhou et al. (2018) [15]	(Fluent) Standard κ-ε model, Lagrangian method, Discrete Phase method (DPM)	Total dust, Respirable dust	Fully mechanized coal mining face; Dust concentration exceeds 1000 mg/m3
Cai et al. (2018) [16]	(Fluent) κ-ε model, DPM	Total dust	Fully mechanized coal face; Dust concentration: 3000 mg/m3
Zhang et al. (2018) [17]	(Fluent) κ-ε model, DPM,	Respirable dust	Fully mechanized mining face under various velocities; Dust concentration: 1440 mg/m3
Hu et al. (2019) [18]	κ-ε model, DPM	Total dust	Around road-header drivers in a fully mechanized excavation face, the peak dust concentration is 3000 mg/m3
Xiu et al. (2019) [19]	(Fluent) κ-ε model, Reynolds time-averaged equation method	Total dust	Fully mechanized caving face; Average dust concentration: 1640 mg/m3
Zhang et al. (2020) [20]	(Fluent) Standard κ-ε model, Eulerian–Eulerian method, Eulerian-Lagrangian method, DPM, Lagrangian method	Total dust, Respirable dust	Down/upwind coal cutting in a fully mechanized mining face; The peak dust concentration: 3000 mg/m3
Zhou et al. (2021) [21]	(Fluent) Eulerian–Lagrangian method, Reynolds time-averaged equation method, κ-ε model, DPM	Total dust	Fully mechanized mining face under different air humidity conditions; Dust concentration at the sidewalk ranges from 763 mg/m3 to 1301 mg/m3
Zhang et al. (2021) [22]	(Fluent) κ-ε model, Eulerian–Lagrangian method, DPM	Total dust, Dust particles with a diameter of over 17 µm, over 12 µm, and below 7 µm	Fully mechanized coal mining face; Dust concentration: 1220–2620 mg/m3
Lu et al. (2021) [23]	(Fluent) DPM, Eulerian–Lagrangian method, κ-ε model, Reynolds averaging method, Stochastic trajectory model	Respirable dust	Fully mechanized longwall mining face; The peak dust concentration: 129,000 mg/m3
Song et al. (2021) [24]	(Fluent) Standard k-epsilon model, DPM, Multi-component transport model	Respirable dust, Gas	Fully mechanized mining face; The peak dust concentration: 2000 mg/m3
Jiang et al. (2021) [25]	(Fluent) Eulerian–Lagrangian method, Standard κ-ε model	Total dust	Fully mechanized tunneling face; The dust concentration: mostly below 400 mg/m3
Zhou et al. (2022) [1]	DPM-DEM, κ-ω model, Wall function	Total dust	Ascensional/descensional ventilation in a fully mechanized coal mining face; Average dust concentration: 1683.3 mg/m3 and 1435.1 mg/m3
Nie et al. (2022) [3]	(Fluent) Eulerian method, Eulerian–Lagrangian method, κ-ε model, DPM	Total dust	Working surface characterized by a large mining height; Average mass concentration (high-concentration area): 1840 mg/m3

Hu et al. (2016) investigated the dispersion behavior of dust following tunnel blasting in coal mines [13]. Their results revealed that respirable particles exhibited alternating dense and dilute phases along the roadway, whereas coarser particles (diameter > 5 μm) tended to settle and accumulate near the floor. Although the initial respirable dust concentration slightly exceeded air quality standards, it rapidly declined to acceptable levels. In contrast, the total concentration of dust generally fell short of air quality standards.

Chen et al. (2018) examined the spatial distribution of airborne dust within the breathing zone along the walkway of a fully mechanized mining face with a 7 m extraction height [14]. They reported that dust generated during coal cutting and hydraulic support advancement accumulated on the leeward side of the shearer, forming a high-concentration belt approximately 36 m in length, with peak values exceeding 2850 mg/m³. Vertically, elevated concentrations were observed in the ranges of 0–1.55 m and 5.55–6.80 m, compared with the intermediate zone (1.55–5.55 m). Based on these findings, a novel dust control system was proposed, achieving an overall removal efficiency of 69.6%.

Similarly, Zhou et al. (2018) conducted numerical simulations to explore airflow–dust interactions beneath a dust extraction fan in a mechanized mining face [15]. The results indicated that during cutting operations, high-concentration dust plumes (>1000 mg/m³) formed within 0–20 m downwind of the shearer and propagated toward the working area along the walkway. To mitigate this issue, a hydraulic axial-flow dust removal fan was developed, reducing dust levels to below 500 mg/m³. Field validation demonstrated removal efficiencies of 66.5% for total dust and 61.8% for respirable dust.

Cai et al. (2018) investigated multi-source dust diffusion in a fully mechanized mining face and analyzed particle size distributions at different locations [16]. A high-concentration zone approximately 2 m in length formed in front of the roller, with peak concentrations reaching 3000 mg/m³. Additionally, a substantial proportion of coarse particles (>70 μm) settled within 40 m of the air outlet.

Zhang et al. (2018) examined the relationship between respirable dust transport and turbulent airflow [17]. At breathing height, respirable dust concentration was negatively correlated with inlet air velocity. As turbulence intensity increased, high-concentration regions gradually diminished; however, when the inlet velocity exceeded 2.5 m/s, the effectiveness of ventilation-based dust control weakened.

Hu et al. (2019) analyzed gas–particle two-phase flow around the road-header driver and observed that the highest dust concentrations recirculated from the rear of the operator position [18]. Similarly, Xiu et al. (2019) identified the location, extent, and intensity of high-concentration dust belts in mechanized mining faces and emphasized that dust dispersion patterns are closely associated with the advancement of hydraulic supports [19].

Moreover, Zhang et al. (2020) illustrated the movement law of dust during coal mining in the downwind and upwind directions of the fully mechanized mining face and optimized the spraying dust reduction device [20]. This improvement increased the total dust efficiency by about 52% and the respiratory dust efficiency by about 43%. In addition, Zhou et al. (2021) stated the law of dust diffusion in the fully mechanized working face under different air humidity conditions [21]. Dust concentration and dust diffusion distance are taken as two evaluation criteria. The simulation results show that increased humidity weakens the dust diffusion ability. Keeping the relative humidity of coal mines at 75~85% is recommended, which benefits miners’ health.

Taking into consideration the severe dust pollution in the 3401 fully mechanized working face of Xin ’an Coal Mine, Zhang et al. (2021) conducted numerical simulations to analyze the spatial distribution of dust concentration and particle sizes, providing theoretical guidance for precise and efficient spray dust removal on the working face [22]. A similar simulation was made by Lu et al. (2021) [23]. They elaborated on the transport of dust particles in the longwall mining face and identified safe areas. It was pointed out that the location and size of the secure areas in the XY and YZ planes are related to the wind speed. However, Song et al. (2021) determined dust dispersion and combined it with the gas spatial distribution to identify two critical areas for prevention and control [24]. Based on similar theory and numerical simulations, Jiang et al. (2021) studied the concentration distribution and diffusion characteristics of dust particles in a fully mechanized mining face with multiple dust sources, indicating that dust particles released from the upper source move upward while those released from the lower source move downward with the airflow [25]. Recently, Zhou et al. (2022) and Nie et al. (2022) found the dust migration law caused by ascensional/descensional ventilation in the fully mechanized coal mining faces and the large-scale mine, respectively, and proposed a new dust removal technology [1,3].

The above studies highlight that numerous publications have investigated coal mine dust diffusion under various mining conditions, with most dust concentrations around 2000 mg/m³, as shown in Figure 4. Most insights into dust movement have been derived from numerical simulations, often complemented by field measurements to ensure accuracy and reliability. Fluent software, together with the κ-ε and DPM mathematical models, is commonly used in these studies, although Zhou et al. (2022) employed the κ-ω model [1].

Figure 4. Dust concentration distribution.

Research typically considers both total dust and respirable dust. Respirable dust is of primary concern for occupational health assessments, as its diffusion directly affects miners’ exposure risk, whereas total dust is mainly studied to understand transport and deposition processes in production environments. High dust concentrations are usually observed at fully mechanized mining faces, making them the most representative and suitable sites for simulation studies due to their complex airflow patterns and dust dynamics.

Overall, these studies demonstrate that coal mine dust movement is influenced by multiple factors, including airflow patterns, mining operations, and equipment layout. Numerical simulations have proven effective in identifying high-concentration dust zones, mapping dust transport pathways, and evaluating the performance of dust suppression technologies such as sprays, foam, and extraction fans. When combined with field measurements, these simulations have further enabled the optimization of dust control strategies, enhancing both total and respirable dust removal efficiencies.

Future research should focus on three main directions: (1) strengthening theoretical understanding and selecting more realistic mathematical models to improve simulation accuracy; (2) emphasizing respirable dust to better safeguard miners’ health; and (3) continuing to use fully mechanized mining faces as the primary research site, given their representativeness and complex dust dynamics.

Existing dust removal techniques in coal mines include dust removers and ventilation systems. Figure 5 summarizes the improvement of dust removal technology based on a comprehensive literature review. Among them, the enhancement of dust removers mainly includes optimizing the structure, operating environment, and chemical modification. The content within the red box presents an analysis of existing dust removal methods based on numerical simulation. Due to the chemical modification of the dust remover being mainly studied by experiment, there are very few papers on chemical modification through numerical simulation alone, so this article will not discuss it. The main contents of the review include the improvement of the dust collector structure, the progress of the dust collector operating environment, and the optimization of the ventilation system.

Figure 5. A summary of the improvement of dust removal technology.

4. Improve the Efficiency of Dust Removal by Numerical Simulation

Numerical simulation of coal mine dedusting uses computational methods to model airflow, dust dispersion, and the performance of dedusting equipment. It involves developing mathematical models, selecting numerical methods, defining boundary and initial conditions, and performing simulations to predict equipment effectiveness, optimizing dust control strategies, and analyzing dust diffusion characteristics. Key aspects include airflow simulation (velocity, direction, pressure), dust diffusion (concentration, particle size, transport paths), and equipment performance (filter resistance, nozzle design, placement). Fluent, a widely used CFD software ANSYS Fluent 2022 R1 by ANSYS Inc., Canonsburg, PA, USA, supports turbulence, multi-phase flow, chemical reactions, and fluid–structure interactions, providing pre- and post-processing tools for geometry setup, solution computation, and result visualization. Its validated reliability across engineering fields makes it a standard platform for modeling coal mine airflow, dust transport, and dedusting equipment, offering theoretical guidance and technical support for effective mine ventilation and dust control [26,27,28].

Dust removers and ventilation systems are commonly used in coal mine dust removal. The following summarizes the research status of the numerical simulation of coal mine dust removal from these two aspects.

4.1. Improvement of Coal Mine Dust Remover

Typically, the dust removal efficacy of a coal mine dust remover is enhanced in three ways: structural Improvement of the dust remover, chemical modification of the medium used for dust removal, and optimization of the operating environment. Compared to numerical simulation, chemical modification for increasing dust removal efficiency is typically performed via experimental methods. Therefore, the following summarizes the dust remover structure improvement and operating environment optimization.

4.1.1. Structural Improvement of Coal Mine Dust Remover

Numerous researchers have optimized the structure of the dust remover by numerical simulation. A summary of the papers on the structural improvement of the dust remover by numerical simulation is listed in Table 3. Chen et al. (2018) designed wet dust-collecting nets applied to a 7 m fully mechanized mining face with 69.6% dust removal efficiency [14]. Liu et al. (2019), Xu et al. (2019), and Xu et al. (2020) simulated three nozzle diameters (1.6 mm, 2.0 mm, 2.4 mm) [29,30,31]. They determined that the nozzle diameters of 2.4 mm provided the most significant dust suppression effect, as well as suction apertures located on both sides of the ejector roller were more efficient. Peng et al. (2020) and Peng et al. (2022) instituted external spray negative pressure mist curtain dust suppression devices and negative pressure jet dust removal water curtain technology [32,33]. At the same time, nozzle installation position, conical cover angle, conical cover length, nozzle type, and the distance between the nozzle opening and the air suction port were optimized, respectively. Arya et al. (2020) proposed a flooded-bed dust scrubber system with a 66.5% dust removal efficacy [34]. Zhang et al. (2020) optimized the opening mode of hydraulic support spraying, significantly improving the total dust and respirable dust removal efficiency [20].

Table 3. Summary of the papers on the structural improvement of the dust remover by numerical simulation.

Authors & Years	Objects	Improvement of Structure	Effects
Chen et al. (2018) [14]	Airflow trajectories; Airflow nephogram; Dust production; Dust concentrations.	Design a wet roller-shutter dust-collecting net	The overall dust-removal efficiency = 69.6%
Liu et al. (2019) [29]	Airflow simulation; Spray fields simulation; Distribution of droplet sizes; Distributions of the spray concentration.	A nozzle with an orifice diameter of 2.4 mm	Produce more droplets; From a higher-concentration spray field with more uniformly distributed droplet sizes; Wrap the cutting head effectively.
Xu et al. (2019) [30]	Change in droplet size with spray pressure and nozzle pore diameter; Spray field concentrations of nozzles.	The diameter of the nozzle pore was 2.4 mm	More than 70% of the target area’s spray field concentration is above 0.04 kg/m2; Dust control efficiency ≥ 78%.
Peng et al. (2020) [32]	Spray jet entrainment airflow; Particle mass concentration; Pressure nephogram; Ejection and suction effects (Velocity vector); Airflow mixing with the spray mist in the dust hood (Velocity vector).	Length of the conical cover: 100 mm; The angle of the conical cover: 15.6°; Nozzle installation position: 10 mm.	At the driver’s location, the reversed loader, and distances of 50 and 100 m from the heading face, dust removal efficiency shows a respective increase of 4.99%, 4.83%, 5.12%, and 3.73%.
Xu et al. (2020) [31]	Spray field concentrations; Purification amount of entrained dust-containing airflow.	A large nozzle diameter (2.4 mm); Suction ports on both sides of the ejector roller.	The concentration of total dust drops below 80.4%. The concentration of respirable dust drops below 81.9%.
Arya et al. (2020) [34]	Velocity gradient; Dust pattern.	Design a flooded-bed dust Scrubber system.	A dust reduction of up to 66.5%
Zhang et al. (2020) [20]	Airflow velocity distribution; Airflow vector; Dust dispersion trajectory; Distribution of dust concentration; Distribution of dust particles.	Propose the optimized opening mode of hydraulic support spraying.	The average dust removal rates of total dust: Downwind: 52.1%; Upward: 53.6%. The average dust removal rates of respirable dust: Downwind: 43.8%; Upward: 42.3%.
Peng et al. (2022) [33]	The experimental platform spray numerical simulation; Droplet concentrations; Air suction volumes; Airflow–spray; Pressure nephogram.	Develop a type of negative-pressure jet dust-removal water curtain. The distance between the nozzle opening and the air suction port: Ls = 6 cm.	The concentration of 92.4% spray = 104 mg/m3. The respirable dust concentration = 0.179 mg/m3; Dust removal rate = 90.4%

In summary, numerical simulations have been widely used to optimize the structure of coal mine dust removers. Studies show that parameters such as nozzle diameter, installation position, spray angle, and suction aperture placement significantly affect dust removal efficiency. For instance, a nozzle diameter of 2.4 mm and dual-side suction apertures enhanced dust suppression, while wet dust-collecting nets, negative-pressure mist curtains, and optimized hydraulic support spraying further improved both total and respirable dust removal. When the inlet pressure reaches 0.1 MPa, the internal airflow of the Laval nozzle can reach sonic velocity, as shown in Figure 6. This will be an important direction for future research. These findings indicate that careful structural design guided by numerical simulation is essential for efficient, targeted dust control, providing theoretical guidance and practical strategies for fully mechanized mining operations.

Figure 6. Flow field distribution of the Laval nozzle.

4.1.2. Optimization of the Operating Environment of Coal Mine Dust Remover

Simulation of the coal mine dust control operating environment is mainly utilized to optimize spraying pressure. A summary of the papers on the optimization of the operating environment of the dust remover by numerical simulation is listed in Table 4. Liu et al. (2019) optimized the sprinkling pressure of an external spraying system to achieve a spraying pressure of 5 MPa, which provided the most effective dust suppression [29]. The distribution of droplet sizes in the formed spray field when a nozzle with an orifice diameter of 2.4 mm was used under a spraying pressure of 1–5.5 MPa. On the other hand, Xu et al. (2020) and Peng et al. (2022) proposed a spray dust-settling device and negative pressure jet dust-removal water curtain technology, respectively, and the conclusion was that 6 MPa had excellent atomization and dust suppression efficacy [31,33]. However, Xu et al. (2019) put forward that the dust suppression effect was evident when the spraying pressure reached 8 MPa [30]. Similarly, Shi et al. (2019) simulated coalbed water infection and concluded that an injection pressure of 8 MPa was generally acceptable, along with other optimized parameters; the injection duration was 10 h, the diameter of the boreholes was 80 mm, and the porosity was 4% [35]. Considering air humidity conditions in the fully mechanized mining face, Zhou et al. (2021) pointed out that the optimal relative humidity was between 75% and 85% [21].

Table 4. Summary of the papers on optimization of the operating environment of the dust remover by numerical simulation.

Authors & Years	Numerical Simulation Parameter	Evaluation Criterion	Optimized Result
Liu et al. (2019) [29]	Pressure water flow: 1/1.5/2/2.5/3/3.5/4/4.5/5/5.5 MPa	Droplet size; Size distribution’s uniformity degree; Actual dust suppression process.	Spraying pressure of 5 MPa
Xu et al. (2019) [30]	Spray pressure of 1/2/3/4/5/6/7/8 MPa	More than 90% of the droplets in the spray field are within the particle size range of 20 to 100 mm; Droplet size distribution is more uniform.	Spray pressure of 8 MPa
Shi et al. (2019) [35]	Injection time: 1/5/10/20/30/40 h; Water injection pressure: 2/4/6/8/10/12 MPa; Borehole diameters: 42/63/80/108 mm; Porosity: 4/6/8/10/12%	Wetting radius; Moisture increment of the coal; Water flow.	Injection time: 10 h; Water injection pressure: 8 MPa; Borehole diameters: 80 mm; Porosity: 4%
Xu et al. (2020) [31]	Spray pressure of 1/2/3/4/5/6/7/8 MPa	A thin fluid membrane; The droplet’s average radius reaches 55 μm; A good performance in dust suppression;	Spray pressure of 6 MPa
Zhou et al. (2021) [21]	Relative humidity: 35%/45%/55%/65%/75%/85%/95%	Effect of dust-dust condensation and dust-wall adhesion; The “human-centered” idea; Safe production and operation efficiency.	Relative humidity: 75–85%
Peng et al. (2022) [33]	Pw = 2 MPa; Pw = 4 MPa; Pw = 6 MPa	Air suction volume of the suction hood (negative pressure); The concentration of the spray field.	Pw = 6 MPa

Numerical simulations have shown that optimizing operating parameters, particularly spraying pressure, is crucial for effective dust suppression. Pressures between 5 and 8 MPa generally provide optimal atomization and dust removal, while additional factors such as injection duration and air humidity (75–85%) further influence performance. Recent studies indicate that dual-phase (air–liquid) nozzles can achieve efficient dust suppression at lower spraying pressures, providing energy savings and enhanced operational safety, shown in Figure 7 [36,37,38]. Precise adjustment of these parameters remains essential for reliable and effective dust control in fully mechanized coal mining operations.

Figure 7. Droplet size distribution of the air–liquid two-phase nozzle at a water pressure of 0.1 MPa.

4.2. Optimization of the Ventilation System

Due to the difficulty of constructing an experimental platform for ventilation systems, numerical simulation is frequently used to study the effect of ventilation systems on dust removal efficacy. The accuracy of numerical simulation can be verified through field measurements. Field measurement is not considered an experiment in this paper. Numerous academicians have extensively researched ventilation systems, including Hua Yun, Nie Wen, Yin Shuai, Liu Qiang, and Geng Fan. A summary of the papers on the optimization of a ventilation system is listed in Table 5.

Hua et al. (2018, 2020) investigated various ventilation strategies, including blower ventilation, long-compression short-suction (LCSS) ventilation, far-pressing-near-absorption (FPNA) ventilation, a multi-radial-vortex-based ventilation system, and a dust-control wind curtain system [39,40,41,42]. Their studies focused on optimizing both the structural layout and airflow parameters of these systems. Structurally, in blower and LCSS ventilation, dust was effectively confined upstream of the driver when the distance between the compressed airflow outlet and the heading end (Lp) exceeded 30 m. For FPNA ventilation, the optimal distance between the generator of multi-radial rotating flow and the tunneling face (LG) was 20 m, whereas the ideal dust-extraction position (ds) in the wind curtain system was 1. Regarding airflow optimization, the recommended pressurized air volume (QP) for FPNA ventilation ranged from 230 to 400 m³/min. In the multi-radial-vortex system, an inflow-to-outflow ratio of 0.75 proved most effective, while the suggested air exhaust rate for the wind curtain system was 330 m³/min.

Geng et al. (2018, 2019) investigated hybrid ventilation and air curtain systems, focusing on the effects of gas velocity, exhaust duct length, and the presence of an air curtain on particulate control [43,44]. Their results indicate that air velocity must be sufficiently high, the vent should be positioned close to the mining site, and the air curtain can effectively reduce spatiotemporal dust concentrations.

Similarly, Yin et al. (2019, 2020) studied air curtain systems with an emphasis on structural optimization [45,46]. Key parameters included the distance between the cutting face and the air-curtain generator (Le), the distance between the forced air opening and the cutting face (LY), the height of the forced air cylinder above the floor (LH), and the distance from the cylinder center to the nearest coal wall (LJ).

Liu et al. (2019) further examined air curtain performance, evaluating the influence of airflow on dust suppression, including the axial-to-radial pressure airflow rate ratio of the air curtain generator (Ra/r) and the exhaust airflow rate of the dust removal fan (Re/p) [47,48]. In the same study, a long-duct forced and short-duct exhaust ventilation system was analyzed, identifying an optimal distance of 35 m between the pressure ventilation outlet and the tunnel face.

More recently, Nie et al. (2022, 2023) investigated FPNA mixed-mode ventilation and airborne air-curtain dust control technologies, analyzing airflow effects, particularly air-absorption volumes (Qe) and other ventilation parameters [49,50].

Other studies have also examined ventilation systems. Zhang et al. (2021) investigated a novel onboard air-curtain dust control method and the effects of turbulent airflow generated by coal cutting (TACC) on dust concentration in the shearer operator’s working area, as well as the contamination characteristics of coal dust [51,52]. Wang et al. (2021) further proposed structural and airflow optimizations for air curtain systems, including adjustments to the forced-air volume and suction region [53]. They found that setting the forced-air volume to 350 m³/min and positioning the suction area near the side wall opposite the forced-air duct minimized the diffusion distance of high-concentration dust. Additionally, Cai et al. (2019) and Xiu et al. (2020) investigated optimal airflow rates in mining ventilation systems, reporting recommended values of 2600 m³/min and 1500–2000 m³/min, respectively [54,55].

Similarly, Mo and Ma (2022) optimized wind speed in a mine ventilation system [56]. In a study analogous to Hua et al. (2018), Cai et al. (2021) examined a far-pressing-near-absorption (FPNA) ventilation system, considering not only the pressurized air volume (Qy) but also the exhaust airflow (Qc) and the height of the pressure duct above the tunnel floor (Dy) [30,57]. Optimal dust suppression was achieved when Qc was 550 m³/min, and Dy was 2 m. Finally, Gao et al. (2021) and Wu et al. (2021) investigated swirling ventilation systems, including wall-mounted swirling ventilation and dual-radial swirl shielding ventilation [58,59]. The optimal blowing-to-suction flow ratios were 0.8 and 1.5, respectively.

Numerical simulations have been widely applied to optimize coal mine ventilation systems, focusing on both structural configurations and airflow parameters. Key factors include airflow rate, wind speed, blowing-to-suction ratios, and the positioning and height of ventilation components. Optimized ventilation modes, such as air curtains, hybrid systems, and swirling ventilation. The air curtain ventilation system is widely applied, as shown in Figure 8. It forms a localized high-speed airflow barrier, which can efficiently, flexibly, and energy-efficiently reduce dust concentrations in the working area. However, simulation outcomes are often site-specific, as geological conditions, roadway geometry, and operational parameters vary significantly among mines. Therefore, field verification remains essential to ensure reliability. Future research should aim to develop more generalized modeling frameworks to improve the generalizability and transferability of simulation results across different mining conditions.

Figure 8. Proportion of different ventilation systems.

Table 5. Summary of the papers on the optimization of the ventilation system by numerical simulation.

Authors & Years	Ventilation Method	Study Contents	Main Finding
Hua et al. (2018) [39]	Blower ventilation; LCSS	Airflow field; Dust dispersion; Lp	Lp > 30 m; Establish a dust-control air curtain
Hua et al. (2018) [30]	FPNA	The migration of dust; LG; QP	LG = 20 m; QP ranges from 230 to 400 m3/min
Geng et al. (2018) [43]	A hybrid ventilation system	Dust dispersion; Gas velocity; Exhaust duct length	The air velocity must be sufficiently high; The vent is close to the mining site
Yin et al. (2019) [45]	An air curtain system	An air-curtain generator’s positions	Le = 20 m
Cai et al. (2019) [54]	A mining ventilation system	Airflow rate; dust particle dispersion patterns; dust concentration	An optimal airflow rate = 2600 m3/min
Geng et al. (2019) [44]	An air curtain system	Spatiotemporal distribution of dust	The air curtain visibly reduces the spatiotemporal concentration of dust, especially near the operator’s location
Liu et al. (2019) [47]	Air curtain ventilation system	Ra/r; Re/p	Ra/r = 1/9; Re/p = 1.2
Liu et al. (2019) [48]	A long-duct forced and short-duct exhaust ventilation system	Distance between the pressure ventilation outlet and the tunnel face	The distance between the pressure ventilation outlet and the tunnel face = 35 m
Xiu et al. (2020) [55]	A mining ventilation system	Dust-removal airflow rate	The optimal dust-removal airflow rate = 1500–1600 m3/min
Wang et al. (2020) [60]	The wall-mounted air duct ventilation	The installation location of the wall-mounted air duct	A wall-mounted air duct should be installed at a distance of 9.6 m
Yin et al. (2020) [46]	An air-curtain dust removal system	LY; LH; LJ	LY = 20 m; LH = 3 m; LJ = 0.6 m
Hua et al. (2020) [41]	A novel multi-radial vortex-based ventilation system	The dust diffusion distance and its concentration	Inflow-to-outflow ratio = 0.75
Hua et al. (2020) [42]	Wind curtain	Wind curtain; Dust suction position (ds); Exhaust air rate (Qs)	Optimal ds = 1; Air exhaust rate = 330 m3/min
Zhang et al. (2021) [51]	A new onboard air-curtain dust control method	Dust concentrations	The concentration of dust in the shearer driver’s working area can be reduced by 69.40%
Gao et al. (2021) [58]	A wall-mounted swirling ventilation	Blowing-to-suction flow ratio; Airflow field structures; Dust concentration distribution patterns	A blowing-to-suction flow ratio = 0.8; Total dust removal efficiency = 90.33%; Respirable dust removal efficiency = 90.33%
Wu et al. (2021) [59]	A dual-radial swirl shielding ventilation	Blowing and suction air volume ratio of the air duct;	Blowing-to-suction volume ratio = 1.5
Zhang et al. (2021) [52]	TACC	The airflow’s streamlines, velocity, and deviated velocity components	Induce the lateral migration of airflow; Induce lateral dispersion of coal dust to the footway space;
Cai et al. (2021) [57]	FPNA	Qc; Dy; Qy	Qc = 550 m3/min; Dy = 2 m
Wang et al. (2021) [53]	Air curtain ventilation system	Forced-air volume; Suction region	Forced-air volume = 350 m3/min; The suction area near the side wall opposite the forced-air duct.
Mo and Ma (2022) [56]	A mining ventilation system	Inlet wind velocities	Wind speed = 2.6 m/s
Nie et al. (2022) [49]	An FPNA mixed-mode ventilation system	Air-absorption volumes (Qe)	The optimal Qe = 575 m3/min
Nie et al. (2023) [50]	An airborne air-curtain dust control technology	Dust pollution diffusion; Air volumes	Air volume = 90 m3/min; Dust control efficiency = 83%;

Table 5. Summary of the papers on the optimization of the ventilation system by numerical simulation.

Authors & Years	Ventilation Method	Study Contents	Main Finding
Hua et al. (2018) [39]	Blower ventilation; LCSS	Airflow field; Dust dispersion; Lp	Lp > 30 m; Establish a dust-control air curtain
Hua et al. (2018) [30]	FPNA	The migration of dust; LG; QP	LG = 20 m; QP ranges from 230 to 400 m3/min
Geng et al. (2018) [43]	A hybrid ventilation system	Dust dispersion; Gas velocity; Exhaust duct length	The air velocity must be sufficiently high; The vent is close to the mining site
Yin et al. (2019) [45]	An air curtain system	An air-curtain generator’s positions	Le = 20 m
Cai et al. (2019) [54]	A mining ventilation system	Airflow rate; dust particle dispersion patterns; dust concentration	An optimal airflow rate = 2600 m3/min
Geng et al. (2019) [44]	An air curtain system	Spatiotemporal distribution of dust	The air curtain visibly reduces the spatiotemporal concentration of dust, especially near the operator’s location
Liu et al. (2019) [47]	Air curtain ventilation system	Ra/r; Re/p	Ra/r = 1/9; Re/p = 1.2
Liu et al. (2019) [48]	A long-duct forced and short-duct exhaust ventilation system	Distance between the pressure ventilation outlet and the tunnel face	The distance between the pressure ventilation outlet and the tunnel face = 35 m
Xiu et al. (2020) [55]	A mining ventilation system	Dust-removal airflow rate	The optimal dust-removal airflow rate = 1500–1600 m3/min
Wang et al. (2020) [60]	The wall-mounted air duct ventilation	The installation location of the wall-mounted air duct	A wall-mounted air duct should be installed at a distance of 9.6 m
Yin et al. (2020) [46]	An air-curtain dust removal system	LY; LH; LJ	LY = 20 m; LH = 3 m; LJ = 0.6 m
Hua et al. (2020) [41]	A novel multi-radial vortex-based ventilation system	The dust diffusion distance and its concentration	Inflow-to-outflow ratio = 0.75
Hua et al. (2020) [42]	Wind curtain	Wind curtain; Dust suction position (ds); Exhaust air rate (Qs)	Optimal ds = 1; Air exhaust rate = 330 m3/min
Zhang et al. (2021) [51]	A new onboard air-curtain dust control method	Dust concentrations	The concentration of dust in the shearer driver’s working area can be reduced by 69.40%
Gao et al. (2021) [58]	A wall-mounted swirling ventilation	Blowing-to-suction flow ratio; Airflow field structures; Dust concentration distribution patterns	A blowing-to-suction flow ratio = 0.8; Total dust removal efficiency = 90.33%; Respirable dust removal efficiency = 90.33%
Wu et al. (2021) [59]	A dual-radial swirl shielding ventilation	Blowing and suction air volume ratio of the air duct;	Blowing-to-suction volume ratio = 1.5
Zhang et al. (2021) [52]	TACC	The airflow’s streamlines, velocity, and deviated velocity components	Induce the lateral migration of airflow; Induce lateral dispersion of coal dust to the footway space;
Cai et al. (2021) [57]	FPNA	Qc; Dy; Qy	Qc = 550 m3/min; Dy = 2 m
Wang et al. (2021) [53]	Air curtain ventilation system	Forced-air volume; Suction region	Forced-air volume = 350 m3/min; The suction area near the side wall opposite the forced-air duct.
Mo and Ma (2022) [56]	A mining ventilation system	Inlet wind velocities	Wind speed = 2.6 m/s
Nie et al. (2022) [49]	An FPNA mixed-mode ventilation system	Air-absorption volumes (Qe)	The optimal Qe = 575 m3/min
Nie et al. (2023) [50]	An airborne air-curtain dust control technology	Dust pollution diffusion; Air volumes	Air volume = 90 m3/min; Dust control efficiency = 83%;

5. Discussion

Dust control in coal mines remains a significant challenge due to the complexity and variability of mining conditions. Numerical simulation has become a critical tool for investigating and optimizing dedusting strategies. Previous research demonstrates that numerical simulations provide valuable insights into three main aspects: structural optimization of dust removal devices, operating environment parameters of dust removal devices, and ventilation system design.

Structural optimization primarily targets the nozzle, focusing on geometry, diameter, installation position, and spacing. Most studies, however, explore only a limited range of parameters, restricting comprehensive investigation of the full design space. This limitation may prevent the identification of superior configurations. Numerical simulations overcome these constraints by enabling broader parameter exploration, reducing experimental costs and time, and generating more precise and systematic optimization results.

The operating environment, particularly pressure optimization, demonstrates similar constraints. Reported optimal pressures range from 5 MPa to 8 MPa across different studies, highlighting the strong site-dependence of the results and their limited generalizability. This variability emphasizes the necessity of multi-site validation and context-specific calibration when implementing simulation-based optimization strategies.

Due to the difficulty of constructing experimental platforms for ventilation systems, their optimization is primarily conducted through numerical simulations combined with field measurements. Optimization efforts generally focus on both airflow and structural parameters. Airflow-related variables include airflow rate, wind speed, and blowing-to-suction ratios, while structural parameters involve the location, spacing, and height of ventilation components. However, the applicability of simulation results remains limited, as optimal values for the same parameters often vary under different coal mine conditions. Moreover, experimental validation of ventilation system simulations is challenging, making field measurements essential to ensure reliability and practical applicability.

With the advancement of molecular dynamics, studies relying solely on numerical simulations to investigate the chemical modification of dust removal devices are expected to become increasingly prevalent, further expanding the scope and precision of dust control research [10,61,62,63].

6. Conclusions

This review summarizes the application of numerical simulation methods in coal mine dust control, emphasizing structural optimization of dust removal devices, operating condition optimization of dust removal devices, and ventilation system design. Key findings include:

Structural optimization: Nozzle design—including geometry, diameter, position, and spacing—plays a central role in dust removal efficiency. Numerical simulations enable broader exploration of parameter space, overcoming the limitations of small-scale experimental studies.

Operating environment: Pressure parameters play a crucial role in dust removal performance. However, existing studies often examine limited pressure ranges, and the reported optimal values vary significantly across different mining conditions.

Ventilation systems: Airflow and structural parameters are essential for controlling dust concentration and distribution. However, optimization outcomes are strongly dependent on mine-specific conditions and require field measurements to ensure reliability.

Future research should focus on developing unified, scalable, and physics-informed simulation frameworks that enhance the generalizability and transferability of results across diverse mining environments. Integration of advanced computational modeling with multi-site validation will facilitate the design of robust dust control systems, ultimately improving both occupational safety and operational efficiency in coal mining operations.