Predictive Analysis of Ventilation Dust Removal Time in Tunnel Blasting Operations Based on Numerical Simulation and Orthogonal Design Method

Abstract

To enhance the understanding of dust diffusion laws in tunnel blasting operations of metal mines and determine optimal ventilation dust removal times, a scaled physical model of a metal mine tunneling face under the China Zijin Mining Group was established based on field measurements. Numerical simulation was employed to investigate airflow movement and dust migration in the tunneling roadway, and the fundamental features of airflow field and dust diffusion laws after tunnel blasting operations in the fully mechanized excavation face were revealed. The effects of three main factors included airflow rate (Q), ventilation distance (S), and tunnel length (L) on the dust removal time after tunnel blasting operations were investigated based on the orthogonal design method. Results indicated that reducing the dust concentration in the roadway to 10 mg/m³ required 53 min. The primary factors influencing dust removal time, in order of significance, were determined to be L, Q, and S. The lowest dust concentration occurs when the ventilation distance was 25 m. A predictive model for dust removal time after tunnel blasting operations was developed, establishing the relationship between dust removal time and the three factors as T = 20.7Q^−0.73S^0.19L^0.86. Subsequent on-site validation confirmed the high accuracy of the predictive model, demonstrating its efficacy for practical applications. This study contributes a novel integration of orthogonal experimental design and validated CFD modeling to predict ventilation dust removal time, offering a practical and theoretically grounded approach for tunnel ventilation optimization.

1. Introduction

China’s rapid economic development has led to a steep rise in the consumption of metal mineral resources. To meet this demand, mining operations have intensified in extraction volume and depth, generating large quantities of dust in underground work environments. This dust poses serious threats to mine safety and to the health of workers [1,2]. During blasting in metal mine tunnels, dust concentrations can momentarily exceed 2000 mg/m, and without proper controls, prolonged exposure to such high dust levels can lead to debilitating occupational lung diseases like pneumoconiosis [3].

Various dust mitigation techniques have been explored to protect miners and improve visibility underground. Water spray systems are commonly used at dust sources to capture particles, and multi-scale simulations of spray atomization have been conducted to enhance their effectiveness [4]. Advanced numerical modeling approaches, such as coupled CFD–DEM simulations, have also been applied to study particle-laden airflow and dust dispersion in mine tunnels [5]. Despite these efforts, heavy dust often clouds tunnels after blasting, severely reducing visibility and exacerbating the risk of accidents or other mine hazards [6,7]. According to reports by the National Health Commission of China, by 2022, the cumulative number of occupational pneumoconiosis cases had reached 915,000, with 11,877 new cases in 2021 alone (accounting for 77.7% of all occupational disease cases) [8]. This alarming situation underscores the urgent need for improved dust control measures in mining operations.

Ventilation remains the dominant method for dust control in metal mine tunneling and blasting operations. To reduce dust diffusion in tunnels, researchers have extensively studied ventilation-based dust control strategies. Nie et al. [9] investigated the time-varying airflow and dust distribution after tunnel blasting and proposed comprehensive dust mitigation measures. Yang et al. [10] combined numerical simulations with field measurements to examine how exhaust ventilation volume affects coupled airflow–dust dispersion in tunnels. Using computational fluid dynamics (CFD) modeling, Geng et al. [11] compared traditional forced ventilation with an air-curtain system, finding that the latter significantly lowered dust concentrations both in the tunnel and at the operator’s position. Jiang et al. [12] simulated dust transport in a one-way tunnel and established optimized ventilation parameters and dust removal methods for tunnel excavation. Pang et al. [13] analyzed gas–solid flow characteristics of blasting dust under different ventilation modes, revealing that a hybrid ventilation mode can more effectively remove respirable particles above the breathing zone than purely forced ventilation, especially in the return airflow roadway. Nie et al. [14] employed a combination of theoretical analysis, CFD simulation, and field experiments—along with an orthogonal experimental design—to evaluate the influence of multiple ventilation factors on coal mine dust dispersion, determining the relative importance of each factor and an optimal combination of ventilation parameters for dust removal.

Beyond ventilation system design, many studies have characterized the dynamics of airflow and dust-particle behavior in tunnels to inform dust control efforts. Shi et al. [15] used individual particle tracking to analyze factors influencing dust movement during blasting. Shi et al. [16] documented temporal variations in dust concentration and particle size at different tunnel locations within 20 min post-detonation. Zhang et al. [17] investigated the spatial distribution of dust concentration and particle sizes in a fully mechanized coal mining face, offering insight into dust characteristics in large excavation spaces. Liu et al. [18] examined dust diffusion in a spiral tunnel under varying ventilation conditions and developed a dust distribution model for that scenario. For high-altitude tunnel blasting, Zhang et al. [19] and Jiang et al. [20] analyzed how altitude, ventilation distance, and airflow rate affect dust dispersion, and they proposed a dynamic model for dust pollution in thin-air environments. Hu et al. [21] studied dust migration in tunnels with different longitudinal slopes, finding that steeper tunnel inclines can prolong dust clearing times after blasting.

Numerous other investigations have further advanced mine dust control knowledge. Liu et al. [22] designed an innovative arc-shaped ventilation device to improve dust extraction in metro tunnel blasting. Nie et al. [23] analyzed optimal ventilation layouts for high-altitude tunnel construction, providing dust control strategies suited to low atmospheric pressure conditions. He and Wang [24] modeled the diffusion of blasting dust in tunnels using CFD simulations, while Huang et al. [25] measured dust concentrations on-site during a large-scale tunnel project (involving both TBM and drilling-and-blasting excavation), yielding empirical data to validate and refine dust dispersion models. A recent review by Si et al. [26] summarizes the progress and emerging trends in mine dust control technologies. Ma et al. [27] investigated dust migration patterns under press-in (forced) ventilation conditions in tunnels, offering guidance for designing effective ventilation dust removal in such scenarios.

Researchers have also focused on specialized ventilation measures and dust suppression techniques. Guo [28] proposed a Gaussian diffusion model to visualize and quantify the spread of blasting dust, and Jiang et al. [29] utilized orthogonal experiments with regression analysis to predict the particle size distribution of dust after spray suppression. Liu et al. [30] studied the ideal blowing-to-suction flow ratio in a shielding ventilation system, whereas Wang et al. [31] demonstrated that a double-radial swirling airflow can significantly improve dust suppression at a tunnel excavation face. Moreover, Chen et al. [32] conducted a CFD-assisted orthogonal study of a forced-exhaust air curtain for tunnel blasting dust removal, and Chen et al. [33] developed a coupled dust-droplet CFD model to optimize spray dust suppression; both approaches achieved notable reductions in dust concentrations.

Additionally, some earlier foundational studies provided important insights. Geng et al. [34] demonstrated the effectiveness of hybrid ventilation in reducing dust dispersion in mine roadways, supporting later findings on combined ventilation approaches. Toraño et al. and Kurnia et al. [35,36] validated CFD models of dust dispersion in development roadways, while Zhou et al. and Nie et al. [37,38] examined the diffusion patterns of respirable dust in fully mechanized mining faces. These works laid the groundwork for many of the recent advances in ventilation and dust mitigation research.

While significant progress has been made in understanding dust behavior and improving dust control technologies, accurately predicting the time required to ventilate and remove post-blast dust from tunnels remains a challenge. Safe re-entry times for workers after blasting are still often determined empirically rather than analytically. In light of this, the present study focuses on a metal mine tunnel face operated by the Zijin Mining Group in China. Using a combination of field measurements and CFD simulations, we examine the airflow and dust concentration distribution under forced ventilation after blasting. We then employ an orthogonal experimental design to analyze the influence of key factors—including ventilation airflow rate (Q), ventilation distance (S), and tunnel length (L)—on the dust removal time (T). Based on these results, a predictive model for ventilation dust removal time is established. The findings provide a theoretical basis for determining safe re-entry times after blasting in metal mine tunnels, thereby contributing to improved occupational health and safety for mine workers.

2. Materials and Methods

2.1. Establishment of Tunnel Model and Mesh Division

The geometric specifications of the metal mine tunneling roadway were derived from an excavation face operated by the China Zijin Mining Group. A scaled construction tunnel ventilation model was developed using SolidWorks 2010, adhering to actual dimensions. The model comprises a tunneling face, an air compression duct, and a tunnel exit. The tunnel extends 180 m in length, featuring a three-centered arch with a width of 4 m, wall height of 3 m, and arch height of 1 m. Simulation employed forced ventilation, utilizing a duct with a diameter of 0.8 m deployed 2.5 m above ground level, with its air outlet situated 30 m from the tunneling face. Figure 1 illustrates the tunnel model. ANSYS Meshing (version 17.2) was adopted to generate a dense tetrahedral mesh for the tunnel’s geometric model. Mesh parameters were configured with a maximum size of 0.9 m, an average size of 0.5 m, and a minimum size of 0.2 m, resulting in a total mesh count of 1,098,653 elements.

2.2. Mesh Independence Verification

Mesh independence verification was conducted to ensure that the numerical simulation results were not influenced by the number of mesh elements [39]. Since airflow is the primary driver of dust diffusion, the airflow velocity at the typical human nose height of 1.5 m was chosen as the evaluation parameter. Compared to the initial study with only three mesh densities, the current verification considers a much wider range of mesh densities, from 0.55 million to 10.84 million elements, as shown in Figure 2.

Figure 2 presents the velocity profiles for a series of tetrahedral mesh configurations generated using ANSYS Meshing. It can be observed that significant differences appear between the coarsest meshes (e.g., 0.55 m, 0.60 m) and finer meshes, especially near the velocity peak at 30 m. As the mesh was refined, the velocity profiles gradually converged. For mesh densities exceeding 1.01 million elements, the curves overlapped almost entirely in both the peak region and the downstream area (80–180 m), indicating that further mesh refinement has a negligible impact on the simulation results.

Therefore, to balance computational efficiency and simulation accuracy, the mesh with approximately 1.01 million elements was selected for all subsequent simulations.

2.3. Numerical Simulation Calculation and Boundary Condition Setup

Prior to conducting numerical simulations, it is necessary to establish certain fundamental assumptions and simplifications regarding the tunnel condition. In this study, the standard k-ε turbulence model was selected to simulate the airflow field, given its computational efficiency, stability, and wide acceptance in simulating fully developed turbulent flows in confined domains. This model has demonstrated reliable performance in tunnel ventilation and dust migration simulations, as supported by prior studies [9,40]. In the analysis of post-blasting dust transport dynamics, the excavation face was designated as the dust diffusion source, with the total mass flow rate of dust at this location derived from the literature review. According to field measurements by Zhang et al. [41], the dust production ratio is 0.0542 kg per kilogram of explosive detonated. In this study, the actual explosive quantity used for a single tunneling blast is about 5 kg, resulting in a calculated dust total mass flow rate of 0.251 kg/s. The computational process involves the initial calculation of the airflow field. Upon convergence of the airflow field computation, a discrete phase model with relevant parameters was implemented to simulate dust diffusion.

Table 1. Boundary condition and parameter settings.

Name	Parameter	Name	Parameter
Solver type	Pressure-based	Solver Time	Transient
Turbulence	k-ε model	DPM Iteration Interval	10
Energy model	Off	Unsteady Particle Tracking	On
Velocity inlet	8.96 m/s	Diameter Distribution	Rosin–Rammler
Turbulent intensity	2.77%	Initial Velocity	−6 m/s
Hydraulic diameter	1.4 m	Total Flow Rate	0.251 kg/s
Outlet type	Outflow	Min. Diameter	1.0 × 10−6 m
Roadway floor	Trap	Max. Diameter	1.0 × 10−4 m
Entrance and exit of roadway	Escape	Mean Diameter	1.2 × 10−5 m
Others	Reflect	Turbulent Dispersion	Discrete Random Orbit Model
Gas phase	Ideal air	Particle density	1.55 kg/m3

displays the specific parameters of the discrete phase model and the associated boundary condition settings.

3. Analysis of Airflow and Dust Movement

3.1. Numerical Simulation of Airflow Field

The distribution of dust inside the tunnel during blasting operations is influenced by various factors, among which the airflow field distribution is the most significant. A comprehensive understanding of airflow characteristics is essential to analyze real-time dust diffusion dynamics during tunnel blasting operations.

Figure 3 presents the simulation results of the airflow field under forced ventilation conditions. The visualization includes airflow streamlines and vector diagrams, illustrating airflow behaviors clearly divided into four distinct regions: recirculation, vortex, multi-directional turbulent, and laminar flow zones. Due to the complexity and overlapping streamlines, certain detailed features may not be immediately distinguishable in the figure. Therefore, enlarged insets are provided to highlight the typical flow structures, particularly in the recirculation and vortex regions, and clear zone divisions are marked directly on the tunnel layout for intuitive interpretation.

Cross-sectional analyses, conducted at 5 m intervals along the tunnel, were employed to measure average wind velocities and identify fundamental airflow patterns. These analyses yielded the following detailed observations:

(1)

Recirculation Zone: Airflow expelled from the duct outlet moves directly toward the tunneling face. Due to shear forces, the high-velocity jet airflow expands continuously. Upon contacting the tunneling face, the airflow is restricted, diffusing outward in all directions and resulting in a distinct recirculation area.

(2)

Vortex Zone (10–30 m): The high-velocity airflow creates a negative pressure zone near the duct outlet, imparting a positive velocity component along the tunnel’s longitudinal (X-axis) direction to the surrounding air. Some air is entrained into the jet flow field, mixing with recirculating air and forming circular vortices. Consequently, wind velocities at the center of this vortex zone are notably lower compared to adjacent areas.

(3)

Multi-directional Turbulent Zone: Beyond the vortex region, airflow becomes increasingly turbulent and multi-directional, generating complex airflow patterns. This region reflects significant airflow disturbances resulting from interactions between various flow streams.

(4)

Laminar Flow Zone: Further downstream, airflow gradually transitions from turbulent to laminar, characterized by stable and uniform flow conditions. The wind velocity stabilizes around 0.3 m/s, marking the laminar flow region.

3.2. Numerical Simulation of Dust Distribution

The numerical simulation of post-blasting dust diffusion in the tunnel involves analyzing different time points and utilizing CFD post-data processing to determine the spatiotemporal distribution of dust. The results are shown in Figure 4 and Figure 5. In Figure 4 and Figure 6, the left color bar (“Dust concentration, kg m⁻³”) applies to the discrete colored spheres—each sphere represents an individual dust parcel, and its color denotes the instantaneous local concentration—whereas the right color bar (“Velocity, m s⁻¹”) applies to the continuous contour shading, illustrating the jet airflow from the duct and the velocity field within the tunnel. As demonstrated by the calculations in these figures, the dust distribution inside the tunnel aligns with the airflow field. Upon detonation, a large amount of dust is expelled from the working face and propagates toward the tunnel exit under the influence of the airflow. At T = 1 min, the airflow near the working face becomes complex, with larger dust particles settling due to gravity, resulting in significant dust accumulation at the anterior end of the working face. The peak dust concentration exceeds 2000 mg m⁻³. At T = 4 min, the turbulent airflow below the air-duct outlet decelerates dust movement. By T = 5 min, a considerable amount of dust has migrated into the laminar-flow region as the airflow velocity significantly decreases. During the initial 5 min, dust traverses the recirculation, vortex, and multi-directional zones before entering the laminar-flow region, steadily advancing towards the tunnel exit. At T = 8 min, the dust reaches the tunnel exit with the highest concentration in the tunnel, measuring 572 mg m⁻³. At this juncture, the blasting has concluded, and the tunnel is permeated with dust.

Figure 6 shows the dust diffusion laws within 53 min post-blasting. It is evident that, at T = 20 min, a significant reduction in dust quantity and concentration is observed at the anterior end of the tunneling face compared to the initial 8 min, with levels declining to about 300 mg/m³. By T = 30 min, the area from the air duct outlet to the tunneling head is largely cleared of dust, with a substantial amount dispersed within the laminar flow region. Throughout the 8–53 min interval, dust is gradually expelled towards the tunnel exit, accompanied by a continuous decrease in concentration.

Figure 7 depicts the temporal variations in dust concentration at the tunnel exit. The curve indicates that dust first appears at the exit 8 min post-blasting, initially increasing in concentration before decreasing, with most of dust cleared by 53 min. The concentration continues to decrease thereafter. Before the blast, the dust concentration on-site was measured at 9.35 mg/m³. Through numerical simulation and the dynamic monitoring of dust concentration, dust removal is considered complete when the highest tunnel concentration drops to 10 mg/m³. At 53 min, the dust concentration in the tunnel approaches 10 mg/m³, indicating that dust removal is essentially complete.

4. Orthogonal Experimental Design for Predicting Dust Removal Time in Tunnel Blasting Operations

4.1. Design of Orthogonal Experiment

Building upon previous research and considering the numerous factors influencing dust removal time, such as air duct position and diameter, it is essential to design experiments reasonably to ensure reliable analysis results and accurately determine the impact of each factor on dust removal time. Based on theoretical analysis and actual on-site conditions, this study focuses on the three most important factors affecting dust removal time, i.e., airflow rate (Q), distance between air duct outlet and excavation face (S), and tunnel length (L). Using the orthogonal experimental design method, a three-factor six-level first-order interaction L³⁶ (36) experimental scheme was developed, as detailed in

Table 2. Variables and parameters influencing dust removal time in tunnel.

Level	Q (m3/s)	S (m)	L (m)
1	2	15	120
2	3	20	180
3	4	25	240
4	5	30	300
5	6	35	360
6	7	40	420

. The dust removal time (T) in the tunnel is set as the target factor, with 36 experiments conducted according to the combinations specified in the orthogonal test design table.

4.2. Analysis of Orthogonal Experimental Results

4.2.1. Orthogonal Experimental Results

The results obtained through numerical simulation based on the factor levels of the orthogonal experiment are presented in

Table 3. Orthogonal experiment calculation results.

Level	Q (m3/s)	S (m)	L (m)	T (min)
1	2	35	360	112
2	3	15	120	44
3	4	25	240	58
4	5	40	420	93
5	6	20	180	31
6	7	30	300	37
7	5	15	300	43
8	6	25	420	62
9	7	35	180	37
10	2	40	180	75
11	3	20	300	71
12	4	30	420	97
13	5	35	360	81
14	6	15	360	47
15	7	25	120	14
16	2	30	120	37
17	3	40	240	65
18	4	20	360	74
19	2	15	240	77
20	4	35	120	32
21	2	20	420	133
22	3	30	180	63
23	4	40	300	59
24	5	30	360	71
25	6	40	120	27
26	7	20	240	32
27	5	25	180	42
28	6	35	300	32
29	7	15	420	48
30	5	20	120	17
31	6	30	240	46
32	7	40	360	45
33	2	25	300	94
34	3	35	420	106
35	4	15	180	35
36	3	20	360	93

. Prior to tunnel blasting, on-site measurements recorded a dust concentration of 9.35 mg/m³. Utilizing numerical simulation for dynamic monitoring, the dust removal process is considered complete when the maximum dust concentration in the tunnel decreases to 10 mg/m³, at which point the time is recorded. The orthogonal experimental results adhere to this criterion, and the dust removal times at different factor levels are illustrated in Table 3 and Figure 8.

Analysis of the dust removal time results from the 36 sets of orthogonal experiments reveals a positive correlation between tunnel length and required ventilation time. For a given tunnel length, the airflow rate shows a significant impact on dust removal efficiency within the tunnel. When tunnel length and airflow rate remain constant, the ventilation distance exhibits a comparatively minor impact on the tunnel ventilation time.

4.2.2. Range Analysis of Orthogonal Experiment

The relative impact of different factors on experimental indicators is determined by the results of the comprehensive average. The range analysis method provides a rational assessment of the relative importance of each factor. Figure 8a shows an inverse relationship between airflow rate and ventilation time, with larger airflow rates corresponding to shorter average dust removal times. Figure 8b illustrates a positive correlation between ventilation distance and dust removal time, with relatively shorter average dust removal times observed at 15 m and 25 m. Considering field conditions, a ventilation distance of 15 m is easily affected by front-end blasting particles, rendering 25 m more appropriate. Figure 8c indicates a positive correlation between tunnel length and dust removal time, with longer tunnels requiring extended dust removal times. Therefore, the factors influencing tunnel dust removal time can be ranked in order of significance as tunnel length > airflow rate > ventilation distance.

4.2.3. Variance Analysis of Orthogonal Experiment

While range analysis can determine the hierarchical order of factors influencing tunnel dust removal time, variance analysis provides insights into the magnitude of impact that airflow rate (Q), ventilation distance (S), and tunnel length (L) have on the tunnel’s ventilation and dust removal time, thereby enhancing analytical precision. The magnitude of the F-value corresponds to the degree of impact. A p-value less than 0.05 indicates a significant difference according to variance analysis.

Table 4. Variance distribution of dust removal time in tunnel.

Factor	Source of Variance	Sum of Squares	df	Mean Square	F-Value	p-Value
Q (m3/s)	Between Groups	79.667	29	2.747	0.651	0.799
	Group	25.333	6	4.222
	Total	105.000	35
S (m)	Between Groups	2257.639	29	77.850	1.121
	Group	416.667	6	69.444		0.486
	Total	2674.306	35
L (m)	Between Groups	347,600.000	29	11,986.207	1.933	0.210
	Group	37,200.000	6	6200.000
	Total	384,800.000	35

presents the F-value for different factors obtained from the variance analysis of dust removal time. The results indicate that F(Q) < P(Q), F(S) > P(S), F(L) > P(L), with tunnel length exhibiting the highest F-value. This suggests that tunnel length has the most significant impact on dust removal time, followed by airflow rate, while ventilation distance shows the least significant influence.

4.3. Mathematical Model Establishment and Field Verification

Using MATLAB 2008, a predictive mathematical model for ventilation dust removal time was developed by fitting the dust removal times from the 36 scenarios derived from numerical simulation. The resulting model is as follows:

$$T=0.71Q^{-0.73}{S}^{0.19}{L}^{0.86}, {\mathrm{R}}^2=0.9103$$

(1)

In this model, T represents the ventilation dust removal time, measured in minutes.

Figure 9 presents a scatter plot comparing numerical simulation data with prediction model calculation data. From the figure, it is observed that the numerical simulation results are predominantly distributed on both sides of the scatter plot of the prediction model, with a limited number of data points deviating from the fitted data. However, the overall trend essentially aligns with the simulation results. Additionally, the coefficient of determination (R²) for this formula is 0.9103. The closer the R² value is to 1, the superior the model fit, indicating that this formula has practical applicability.

To verify the accuracy and practicality of the aforementioned predictive model, dust removal times were measured on-site under different conditions during tunnel blasting operations. A GCG1000(A) type mining dust concentration sensor was installed at the tunnel exit for the remote online monitoring of dust concentration, as shown in Figure 10. The dust removal time was recorded when the dust concentration at the tunnel exit decreased to 10 mg/m³. The on-site verification corresponded to Q = 5 m³/s and S = 30 m, with measurements taken when the tunnel had advanced to 180 m, 240 m, and 300 m.

Table 5. Comparison of on-site measured and predicted model calculated dust removal times.

L (m)	On-Site Measurement Time (min)	Simulate Ventilation Time (min)	Error
180	40	37	7.50%
240	51	47	7.84%
300	54	57	5.26%

displays comparisons between the on-site dust removal times and the data generated by the calculation formula. The comparison reveals that the relative error between the on-site measurements and the predicted model’s calculated ventilation dust removal times falls within 10%, essentially meeting the requirements for estimating on-site dust removal times. This level of accuracy indicates the efficacy of the developed formula.

5. Conclusions

This study investigates the excavation face of a metal mine operated by the China Zijin Mining Group, utilizing a scale simulation model for a forced ventilation workface. Numerical simulations were conducted to analyze airflow patterns and the complete dust removal process within the tunnel. Initial findings indicate that 53 min are required for the dust concentration in the tunnel to reduce to 10 mg/m³ after blasting.

By employing an orthogonal experimental design, the effects of airflow rate (Q), ventilation distance (S), and tunnel length (L) on dust removal time were systematically evaluated. Results demonstrate that tunnel length is the dominant factor affecting dust removal time, followed by airflow rate and then ventilation distance, with an optimal ventilation distance identified as 25 m. A predictive model was established to describe the relationship between dust removal time and the three principal factors, achieving an R² value of 0.9103, which indicates a strong correlation and good model reliability.

Further, on-site verification of the model revealed that relative errors between predicted and measured dust removal times were consistently within 10%, confirming the robustness and practical applicability of the proposed model.

Moreover, this study found that increasing the airflow rate leads to a sub-linear reduction in dust removal time—specifically, raising the airflow from 2 m³/s to 6 m³/s (a threefold increase) can reduce clearing time by approximately 72% but further increases beyond 6 m³/s yield only marginal improvements while incurring disproportionately higher energy costs. Thus, an optimal airflow rate of 5–6 m³/s is recommended for similar tunnel conditions to achieve efficient dust removal with reasonable energy consumption.

In summary, this research provides a novel, quantitatively validated predictive framework for dust removal in tunnel blasting operations, which not only deepens the theoretical understanding of dust diffusion dynamics but also offers practical guidance for optimizing ventilation strategies in metal mine tunnels.