Block Cave Mine Ventilation: Research Findings

Abstract

The primary objective of this research is to provide practical mine ventilation engineering tools (i.e., cave resistances and pollutant emission rates) to model and predict adequate airflows and pressure drops across the cave with respect to cave propagation in underground block or panel cave mines. We used several research methods to investigate the phenomenon of cave ventilation and pollutant gas emissions in block or panel cave mines. The research methods include computational fluid dynamics (CFD)—continuum and discrete approaches in conjunction with advanced geo-mechanical analysis through numerical modeling, scale model studies, mathematical modeling, field observations, discrete fracture network (DFN), flow through porous media, particle flow code (PFC), Ventsim, MATLAB, and Python programming. The study investigated the several research questions related to block or panel cave mines: immature and mature cave properties, radon and airflow behavior, radon control measures, cave characteristics, ventilation on demand, blasting fumes, prediction of porosity, and permeability of different cave zones, the effect of undercut ventilation, forcing, exhaust and the push-pull system, the effect of airgap, and broken rock porosity and permeability on the cave ventilation system. The findings from this study provide useful information for optimizing the block or panel cave mine ventilation systems.

1. Introduction

In the panel caving method, the caving process begins with the ore blasting, then drawing the broken ore from the draw points located at the production level. The extraction of broken ore creates a void volume inside the cave. This void (known as an airgap) and gravity do the rest of the work in breaking the ore in the cave. This rock-breaking process continues as the broken ore is withdrawn from the draw points (Figure 1).

Figure 1.

Panel caving schematic [1].

The cave initiation is when the caving activity begins, and the hydraulic radius starts to form. As a result of stress increment after the blasting, a stable arch forms in the rock mass. However, the arch cannot resist gravitational stresses indefinitely, and as the cave propagates and the hydraulic radius continues to increase, rock failure will re-initiate. The hydraulic radius at which propagation is achieved can be interpreted as the limit of cavability. However, caving can only actualize when the cave draw starts, and an airgap is created by removing the support provided by the caved rock mass [2].

At the study site (panel cave mine), the panel arches over with a maximum height of 550 m. The ore body rock mass rating (RMR) ranges from 27 to 60, with uniaxial compressive strengths typically ranging from 100 to 275 MPa. Although this is at the high range for caving, there have been minimal problems initiating and advancing the cave because of the lubricating property of the mineral and fillings on the geologic structures [3].

It is already known that gravity and the stress induced in the crown or back of the undercut or cave are the two major factors that trigger the caving event. Caving occurs in two distinct situations—a low-stress environment, where gravity falls due to the lack of confinement is the dominant mechanism; the other extreme, in which the induced tangential stresses are high compared with the compressive and shear strength of the rock mass. This form of caving is often referred to as stress caving [2].

In the caving mining methods, assessing the initiation and growth of caving in rock masses is important to determine the higher-production, lower-cost method. Currently, experience and empirical methods based on the rock mass characterization, such as rock quality designation (RQD), Norwegian Geotechnical Institute’s Q system, and rock mass rating system (RMR), are integrated to predict the hydraulic radius for sustained cave growth and the resulting “break” angles and propagation rates of the cave as it grows to the ground surface.

Although this mining method seems most straightforward, it has to be designed carefully; otherwise, the rock will not break properly; hang-ups will develop in the cave. This will result in the development of a large airgap, which can create dangerous conditions in the form of air blasts.

1.1 The sequence of operations in a panel cave mining

The sequence of the caving operation starts by advancing the undercut level. A set of parallel and horizontal tunnels is created to develop the upper cavern of broken rock. In the second phase of the production, parallel to the undercut level, the production level is advanced. To collect ore beneath the rock mass, vertical holes are drilled to form funnel-shaped structures called drawbells, which are created above the production level, and extend to the undercut level. The broken ore is extracted from draw points and loaded into the equipment to deliver to the ore passes or an underground crusher.

1.2 Panel and extraction layout

The layout of both the panel and the extraction level is one of the most critical tasks in the planning of caving mines. Panel layout design represents a balance between mitigating technical risks and maximizing project value. It aims to minimize surface subsidence risks, minimize abutment stress damage, avoid alignment with major geologic structures, maintain a manageable undercut face length and advance rate and maximize the project’s net present value (NPV) Pascoe [4].

The extraction layout seeks to maximize the recovery, minimize the dilution, and increase the efficiency of the ore handling system. In designing the suitable extraction layout, gravitational flow and dilution should be taken into account. However, because of the uncertainty of the in situ rock mass, an accurate gravitational flow evaluation of material movements is not possible. In addition, dilution is a dynamic process and has a self-mixed property, which means that broken ore can easily mix with waste material or low-grade rock that is located in the upper portion of the columns. Since the fine particles move faster to the drawpoints, the percolation of waste material decreases the grade of the drawn material. A schematic sequential drawbell section showing the lateral dilution mechanism is presented in Figure 2.

Figure 2.

Lateral dilution mechanism [5].

1.3 Caving zones

Figure 3 depicts the conceptual model of caving consisting of five major zones/regions. The conceptual model was based on the analysis of data collected at Northparkes Mines’ E26 block cave, Australia [6] and consists of the following zones:

1. Caved zone (mobilized zone): This region consists of broken ore blocks that have fallen from the cave back. The material in the caved zone provides support to the cave walls. This is the bottom-most region close to drawbells.

2. Airgap: Extraction of broken ore creates a void volume inside the cave, and this region is called the airgap. During continuous caving, the height of the airgap formed is a function of the extraction rate of the material from the caved zone.

3. Zone of discontinuous deformation (yield zone): This region no longer supports the overlying rock mass and adheres to large-scale displacements of rock.

4. Seismogenic zone: An active seismic front occurs due to slip on joints and brittle failure of rock, mainly due to changing stress conditions caused by the progress of the cave.

5. Surrounding rock mass: Elastic deformation occurs in the rock mass ahead of the seismic front and surrounding the cave.

Figure 3.

Conceptual model of caving [6].

An interesting point to note is that caved zone is the highest porosity region in the cave (if the airgap is not considered). Over time, as the cave evolves and progresses, the caved zone reaches the economic ore boundary, defined as “fully developed” in terms of ore production. The cave is then termed as a “fully developed” or “mature cave.” A mature cave consists of different porosity zones consisting of broken ore and waste (based on the degree of dilution) of various sizes in the caved area with zero airgap. The particles’ size changes from finer to coarser as we move from caved zone to the surrounding rock mass zone, as shown in Figure 3.

1.4 Radon

Radon gas is a major source of ionizing radiation [7] due to the release of harmful radiation during its decay [8]. According to reports from the radon epidemiology subgroup, about 1100 radon-induced lung cancer deaths occur each year in the UK [9]. About 21,000 radon-related lung cancer deaths occur in the US [10]. The severity of these effects on human health prompted multiple investigations that focused on radon mitigation measures. Hence, it is one of the most extensively investigated carcinogens with about 1 million radon-related indoor measurements taken annually in the US [11]. Several measurements and mitigation methods are available to detect and control radon in underground mines [12].

This chapter discusses the recent research investigations related to block or panel cave mines on the following topics—immature and mature cave properties, radon and airflow behavior, radon control measures, cave characteristics, ventilation on demand, blasting fumes, prediction of porosity, and permeability of different cave zones, the effect of undercut ventilation, forcing, exhaust and the push-pull system, the effect of airgap, broken rock porosity and permeability on the cave ventilation system.

2. Research methods

The specific objectives of this study include:

• to develop a better understanding of the effect of airgap geometry on the cave airflow resistance and the radon emission rates from an immature panel cave

• to predict radon gas emissions from fractured rocks and their control measures in block/panel cave mines

• to predict the porosity of different cave zones in a typical block or panel cave mine under both cave development and mature cave conditions.

• to predict cave airflow resistance and gas emission rates from a mature panel cave under varying cave conditions

• to investigate the blasting fume distribution in a typical block/panel cave mine.

To achieve the stated objectives, the following tasks were completed:

1. Development of a panel cave geometry model.

2. Development of CFD (using continuum approach) models of an immature panel cave with varying airgap geometries.

3. Simulation of airflow and radon gas flow in immature and mature panel caves.

4. Analysis of the effect of airgap geometry on the cave airflow resistance and radon working levels in a panel cave mine.

5. Development of a robust model for predicting radon flux through fractured rocks.

6. Development of a numerical model for investigating proactive radon mitigation measures in a developing and developed cave mine.

7. Development of a model for predicting airway resistance in cave mines.

8. Investigation of the effect of production rate and rock mass strength on the porosity of different cave zones using a continuum approach (FLAC 3D).

9. Examination of the effect of particle size distribution and production draw control strategy on the porosity of the mobilized zone (fragmented rock) in a fully developed cave (mature) using a discontinuum approach (PFC 3D).

10. Investigation of the behavior of blasting fumes in a block cave mine.

11. Analysis of the effect of changes in the cave bulk porosity on the airflow resistance and radon concentration (WL)s found in a panel cave mine.

12. Investigation of different ventilation control approaches to minimize the radon flow in an underground mine using a scaled model constructed in the lab.

13. Investigation of the airflow behavior through a mature cave under changing cave conditions: cave permeability and porosity, and airgap.

14. Examination of the airflow behavior through a single cave column under changing cave conditions: particle size, cave size, and cave porosity.

15. Investigation of the airflow behavior through a mature cave under multiple fan configurations and various cave conditions: varying cave permeability and porosity, undercut structures, and different negative pressures on the cave.

16. Investigation of the airflow behavior through a mature cave under multiple regulators and changing cave conditions: varying cave permeability and porosity, undercut structures, cave footprint, and regulator combinations.

3. Results

This section presents the key findings from the block cave mine ventilation research study that provides valuable information for optimizing the ventilation systems. The findings include the results and observations from numerical simulation studies, scale model studies of the mature and immature block and panel cave mines, mathematical modeling, and field observations.

3.1 Finding 1: airgap and airflow resistance of an immature panel cave

Airflow through an immature panel cave was analyzed under four different airgap heights using a 3D panel cave geometry model (Figure 4a and b). Furthermore, CFD simulations were performed to predict the radon emissions from an immature panel cave.

Figure 4.

(a) Model dimensions—Top view (meters). (b) Model dimensions—Front view (meters).

The analysis of the airflow patterns through the cave indicated that the size and intensity of the recirculation zones change with the change in airgap heights. CFD simulation results show that in the absence of undercut ventilation, radon concentrations in the production level were much lower than those observed when the undercut level was ventilated [13, 14]. This can be attributed to the creation of a low-pressure region in the undercut level, the porous nature of the cave, and the air recirculation (Figure 5) in the cave.

Figure 5.

Velocity vectors.

3.2 Finding 2: cave porosity zone height decreases with an increase in rock mass strength

Porosity values for different cave zones were predicted under both cave development and mature cave conditions using FLAC3D and PFC3D modeling, respectively. FLAC3D simulations were performed first to predict the formation of different zones in a typical block cave mine and then to investigate the effect of rock mass strength (RM) and production rate on the porosity of different cave zones in both block and panel cave mines.

Simulations were performed on two models for both block and panel cave cases by keeping all the model parameters the same except the properties of the rock masses. For both cases, production draw has been simulated with a total height of draw (HD) of 0.6 m by applying the downward velocity of 0.0001 m/s at all the grid points on the roof of the undercut. However, in the case of a panel cave simulation, the applied extraction rates are not the same at all the grid points. Simulated porosity values are shown in Figures 6 and 7 for block and panel cave, respectively.

Figure 6.

Porosity value profile for RM1 (left) and RM2 (right) for block cave mine model.

Figure 7.

Porosity value profile for RM1 (left) and RM2 (right) for panel cave mine model.

It can be seen from Figures 6 and 7 that as the rock mass strength (RM) increases, the heights of the cave porosity zones decrease for both block and panel cave models. This is because the propagation rate decreases as rock mass strength increases. For both cases, the porosity value for the mobilized zone ranges from 0.35 to 0.40. Further, the zone heights for different cave zones are relatively higher for RM1 than RM2.

Similar trends were observed for the panel cave case, except that the cave zone profile is inclined toward the right due to the nature of the extraction for the panel cave mine.

3.3 Finding 3: cave porosity zone height decreases with a decrease in extraction rate

Four different scenarios were simulated with increasing velocities from 0.0001 to 0.0004 m/s by keeping the same rock mass properties (RM2). For the block cave model, the same velocities (extraction rates) are assigned at all the grid points, whereas in the panel cave model, different velocities are applied at grid points due to the inherent nature of the caving process. An increase in velocity value means an increase in material extraction rates. The simulated porosity values for different zones under different extraction rates for both block and panel cave models are shown in Figures 8 and 9.

Figure 8.

Porosity value profile for RM2 under four different extraction rates (0.0001–0.0004 m/s) for block cave model.

Figure 9.

Porosity value profile for RM2 under four different extraction rates (0.0001–0.0004 m/s) for panel cave model.

In the real-world scenario, in a propagating cave, the cave height increases with increased production. It was evident from Figures 8 and 9 that when the ore extraction rate was increased, the cave zones height was increased. The mobilized zone porosity tends to be higher as it is the actively flowing region in the cave. In both block and panel cave models, the mobilized zone porosity value ranges from 0.35 to 0.40. The height of this zone is relatively high compared to the other zones due to its proximity to the drawpoints where the ore is extracted from the cave.

In the block cave model, when different velocities, from 0.0001 to 0.0004 m/s, were applied at the drawpoints, the porosity values in the mobilized zone were observed to be from 0.30 to 0.35. This could result from stagnant flow in the cave that subjected the material to re-compaction. If the extraction rate of material is not equal to the rate of cave propagation, the material will stagnate, re-compacted, and result in lower porosity.

3.4 Finding 4: cave porosity is affected by the fragmentation size

Material extraction is simulated in a block cave model by opening drawpoints to extract a targeted mass of 100,000 metric tons. As soon as the drawpoint is open, due to gravity, the spheres (broken rock) will start flowing toward the drawpoints. Measurement locations are strategically placed to measure the porosities as the material flows through the cave. Porosity measurement histories are recorded at all the measuring locations (spheres) while extracting the material from the cave through the drawpoint. From the simulation results, it was found that the porosity change in the isolated extraction zone (IEZ) ranges from 0.39 to 0.56. In comparison, in the isolated draw zone (IDZ) insignificant porosity change (0.38–0.42) was observed. The random spike(s) seen on the graph shows the change in the porosity while the material is drawn from the cave.

To study the effect of particle size distribution on the porosity change, two fragmentation distributions with different mean particle sizes and standard deviations are applied for the block cave mine. Table 1 illustrates the particle distributions considered for the simulations.

Layer	Mean particle size in meter		Standard deviation in meter
	Fragmentation #1	Fragmentation #2	Fragmentation #1	Fragmentation #2
1	1	1.5	0.1	0.5
2	2	2.5	0.1	0.5
3	3	3.5	0.1	0.5
4	4	4.5	0.1	0.5

Table 1.

Gaussian particle size distributions for fragmentation #1 and #2.

Scenario #1 is simulated by consecutively opening the six drawbells. When the target discharge mass is reached the assigned value in the system, the next drawpoint will open immediately as shown in Table 2.

Mass discharged in kg (in million)	Scenario #1 drawpoint id opened	Scenario #2 drawpoint id opened
0	1	1
1	2	6
5	3	2
10	4	5
20	5	3
30	6	4

Table 2.

Discharge mass criteria for opening the drawpoints for two scenarios.

Scenario #2 is conducted by opening the six drawbells randomly. The discharged mass is applied to control the opening of the drawbells for the random opening. A summary of discharge mass criteria and the drawbells are provided in Table 2.

The numerical model of porosity assessment using a discontinuum approach successfully modeled change in porosity associated with the fragmented rock mass flow in a mature cave. It was found that during material extraction, the porosity changes relatively higher in IEZ than in IDZ. These changes for IEZ range from 0.39 to 0.56 for a block cave and 0.38–0.48 for a panel cave.

The sensitivity analysis on particle size distribution concluded that fragmentation size affects cave porosity. In the case of Fragmentation #1, the change in porosity ranges from 0.40 to 0.48 in IEZ and from 0.38 to 0.56 for Fragmentation #2. Similarly, the draw control strategy also affects cave porosity. In the case of Scenario #1, the change in porosity ranges from 0.38 to 0.52 in IEZ and from 0.38 to 0.48 for Scenario #2 in IEZ.

3.5 Finding 5: in a fully developed cave, undercut level ventilation increases the radon concentration in the production drift

Figure 10 shows the velocity contours through a section of the model (the production drift and draw points). The velocity magnitude through the first and second drift is similar; however, it is higher in the third drift due to the influence of the undercut ventilation. Based on the proximity of the undercut drift to the third drift, most of the airflow from the undercut duct flows out through the draw bells in the third drift, creating a significant difference in the magnitude of velocity.

Figure 10.

The magnitude of velocity through the production drift.

The static pressure decreases down the production drift due to wall friction and shock losses through the draw points. The drawpoint—drift configuration is similar to a “T-junction” with orientation, which introduces flow separation [15]. Therefore, the number of drawbells (shock loss sources) in each drift affects the total pressure drop in the drift. In addition, for each drift, the distance between the inlet and the first draw bell affects the developing flow due to momentum loss from the drawbells. Therefore, the static pressure is maximum in the third drift based on the distance.

In addition, we analyzed the static pressures in the localized regions (Figure 11a) and the shock losses for both sides of the second drift (Figure 11b). We observe a notable variation in pressure around region “B” compared to region “A”; hence the shock loss is greater for region “B” due to the orientation of the drawbells. Since air flows from right to left, the flow makes a 56° turn to get into drawbells around region “B,” and a 24° turn into region “A.” Therefore, as the bend angle increases, shock loss increases [16], hence the airflow pressure is more efficient with drawbell that requires a less angle of airflow rotation (Region A of the second drift, and the first drift).

Figure 11.

(a) Static pressure contours through the production drift; (b) localized pressure distribution from a section of the second drift.

Figure 12 shows the working level through the three production drifts, and based on the number of radon sources, the second drift (with 13 sources) has the largest region with high concentrations compared with the first drift (with seven sources), and the third drift (with six radon sources).

Figure 12.

Radon concentration through the production drift presented in working levels to represent the concentration of harmful radon daughters.

The concentrations are lowest in the third drift due to the combined effects of the pressure distribution, the number of radon sources, and the undercut ventilation. However, there are small regions with a high concentration around the last drawbell due to the impact of undercut ventilation. Most of the airflow from the undercut duct flows out through the third drift with a high radon concentration from the cave. Figure 13 shows the working level through a section of the cave along with the undercut drift. The positive pressurization due to the undercut ventilation effectively prevents the influx of radon into the undercut drift. Therefore, personnel working in the undercut drifts are not exposed to high concentrations with this ventilation design.

Figure 13.

Radon concentration through a section of cave presented in working levels.

Figure 14a shows a plot of radon concentration along the center of the first, second, and third production drifts to understand the changes in radon concentration through the production drift. The concentrations increase non-linearly as the number of radon sources increases down the drifts. However, there is a drop in concentration for the first drift due to the distance from the last source of radon, which is the farthest. Hence, based on the drift layout/configuration, specific locations other than the outlet might have higher radon concentrations. As observed in Figure 14a, the non-linear increase is due to the differences in radon sources (drawbells) compared to the wall sources, which increases linearly from previous studies [17]. For further investigation, Figure 14b compares the concentration at the outlet of the three production drifts at different times. The concentrations increase with time, but the rate decreases after about 4 hours as the flow develops. The results presented in Figure 14a and b are from the center of the production drifts; however, radon concentration varies across the drift cross-section for a fixed location.

Figure 14.

(a) Growth of radon along the center of the production drift for the first, second, and third, after 9 hours. (b) Radon concentration at the end of the production drifts w.r.t time.

3.6 Finding 6: with undercut ventilation, negative pressure on the cave top effectively reduces radon concentration through the production drifts

In some instances, a regulator is used along with the exhaust fan to maintain negative pressure on top of the cave to remove pollutants from the cave due to the pressure difference. We conducted an independent study to understand the impact of negative pressure (300 Pa) [18] on top of the cave for the result presented in Figure 12. Figure 15 shows a significant reduction in radon concentration through the production drift. Therefore, imposing a negative pressure on the cave top, positive pressurization of the production, and undercut drifts effectively reduce radon concentration through the production drifts.

Figure 15.

Effect of negative pressure (boundary condition for cave top) on radon concentrations in the production drifts.

3.7 Finding 7: relationship between pressure drop and airflow

The study investigated two ventilation stages of a fully developed panel cave: with and without undercut ventilation using discrete and continuum CFD models [19]. With active undercut ventilation, we found that—(1) the cave act as a unique form of porous media, which influences flow through the production drifts such that the pressure drop model (in the production drifts) does not agree with either the existing models, hence, a unique model is required for the ventilation design, which provides opportunities for further research; and (2) increase in cave porosity decreases the drift’s resistance to airflow. Without the undercut ventilation, the cave has less effect on the production drift ventilation as the pressure drop model agrees with the turbulent model; and an increase in cave porosity decreases the drift’s resistance to airflow as shown in Figures 16 and 17 (Tables 3 and 4).

Figure 16.

Static pressure distribution under undercut ventilation (left) and without undercut ventilation (right) using a discrete model.

Figure 17.

Comparison of drift resistance with and without the undercut ventilation (nu—no undercut).

Drift	Discrete model	Continuum model
First drift	$\mathit{\Delta p}=0.0127Q^{1.7725},R^2=0.999$	$\mathit{\Delta p}=0.0102Q^{1.8201},R^2=0.9998$
Second drift	$\mathit{\Delta p}=0.0122Q^{1.8064},R^2=0.9994$	$\mathit{\Delta p}=0.0106Q^{1.8468},R^2=0.9999$
Third drift	$\mathit{\Delta p}=$0.0106$Q^{1.8437},R^2=0.9995$	$\mathit{\Delta p}=0.0099Q^{1.8547},R^2=0.9999$

Table 3.

Comparison of pressure drop equation for model with undercut ventilation.

Drift	Discrete model	Continuum model
First drift	$\mathit{\Delta p}=0.0052Q^{1.9585},R^2=1$	$\mathit{\Delta p}=0.0046Q^{1.9878},R^2=1$
Second drift	$\mathit{\Delta p}=0.0053Q^{1.9816},R^2=1$	$\mathit{\Delta p}=0.005Q^{2.0042},R^2=1$
Third drift	$\mathit{\Delta p}=0.0047Q^{2.0155},R^2=1$	$\mathit{\Delta p}=0.0047Q^{2.0142},R^2=1$

Table 4.

Comparison of pressure drop equation for model without undercut ventilation.

3.7.1 Effect of porosity on cave pressure drop

For a given porosity of the cave and airflow in the production drifts, the pressure difference across the cave was calculated and then plotted against the airflow quantity flowing through the cave, as shown in Figure 18. For example, five different air quantities were simulated for a bulk cave porosity of 35%. Therefore, for six different bulk cave porosities, a total of 30 simulations were performed to develop the pressure-quantity (P-Q) characteristic curves for a mature panel cave mine. Figure 18 shows the variation of the airflow resistance value with respect to the bulk cave porosity.

Figure 18.

P-Q characteristic curves for a mature cave under different porosity conditions.

3.8 Finding 8: relationship between airflow and radon concentration

As per the Mine Safety and Health Administration (MSHA) regulations, personnel shall not be exposed to air-containing concentrations of radon daughters exceeding 1.0 WL. No person shall be permitted to receive exposure over 4 WLM (Working Level Months) in any calendar year. 30 CFR 57.5005 suggests dilution with uncontaminated air to mitigate radon exposure. Therefore, we studied the effect of airflow on radon concentration [20, 21]. The airflow through the production drifts is increased, and radon concentration at the outlets of the drifts is measured after 8 minutes. Figure 19 show radon concentrations with airflow at the outlets of the first, second, and third drifts.

Figure 19.

Effect of increasing airflow on working level at the outlet of the (a) first drift; (b) second drift; (c) third drift.

McPherson (1993) stated that if an airway is supplied with uncontaminated air and the rate of radon emanation remains constant, then the exit working level of radon daughter is proportional to the residence time $\left({\mathbf{t}}_{\mathbf{r}}\right)$raised to the power of 1.8 Eq. (1).

$$\mathit{WL}\propto {\left({\mathit{t}}_{{r}}\right)}^{1.8}$$E1

The results show that the radon concentration decreases with increased airflow, and an empirical relationship is developed for each drift. Due to the difference in the number of radon sources, the relationship varies for the three drifts. Therefore, based on the drift’s configuration, the empirical relationship between the working level and airflow might be different, unlike in Eq. (1).

3.8.1 Comparison of discrete and continuum model

The airway characteristics are analyzed with the help of a discrete and continuum model [19, 22]. Both models show that radon growth through the production drift is non-linear; however, the continuum model does not replicate the significant variation in radon concentration with time compared to the discrete model [23]. Basically, the continuum model indicates that, beyond a specific time, consistent airflow keeps the concentration constant with respect to time. This is further verified in Figure 20, which compares the radon concentration at the outlet of the second production drift for both models.

Figure 20.

Comparison of the discrete and continuum models for predicting radon levels at the outlet of the second drift with time.

The discrete model demonstrates that the concentration increases with time, unlike the continuum model, which shows that after about 5 hours, the radon concentration is almost steady. In addition, we replicated the study on the effects of airflow using the continuum model. The empirical relationships developed are compared with the discrete model in Table 5, and both models suggest that different relationships are required for the drifts based on their configuration.

Drift	Discrete model	Continuum model
First drift	$\mathit{WL}=127.05Q^{-1.42},R^2=0.9997$	$\mathit{WL}=845.87Q^{-2.01},R^2=0.9918$
Second drift	$\mathit{WL}=33.17Q^{-1.04},R^2=0.9995$	$\mathit{WL}=85.86Q^{-1.57},R^2=0.9962$
Third drift	$\mathit{WL}=47.17Q^{-1.28},R^2=0.9995$	$\mathit{WL}=24.07Q^{-1.20},R^2=0.9998$

Table 5.

Empirical relationships—Effect of airflow on radon concentration for the first stage of a fully developed cave.

3.9 Finding 9: characteristics of model with no undercut ventilation

This section considers the fully developed panel cave without the undercut ventilation (second stage). This is usually after the whole panel is developed, and the undercut level no longer exists. No flow is assigned to the undercut inlet duct and outlet to represent this condition, but all other conditions presented in the previous section are used. Figure 21 shows the velocity contours through the drifts without the undercut ventilation. Unlike in Figure 10 (with undercut ventilation), the velocity magnitude through the three drifts is more uniform. However, based on the number of pressure loss sources (drawbells), the magnitude of velocity through the first (seven shock loss sources) and third (six shock loss sources) drifts are pretty similar.

Figure 21.

The magnitude of velocity through production drift without undercut ventilation.

Figures 22 and 23 show the concentration through the production and undercut drifts, respectively, after 9 hours. The concentration through the production drift (Figure 22) is significantly lower than in the first stage with undercut ventilation (Figure 12).

Figure 22.

Radon concentration in the production drift without the undercut ventilation.

Figure 23.

Radon concentration through the production drift is limited to 0.2 WL.

This suggests that the undercut ventilation increases radon concentration in the production drift by transporting radon generated within the cave into the drifts. Even though the airflow is almost uniform through the production drifts, the locations of the maximum concentration vary inside the drawbells—Figure 22. To investigate this, Figure 23 shows a localized image of the concentration with a 0.2 WL limit. The drawbells are oriented in two directions (340 and 560) to the flow based on the flow direction. The shock loss is greater with the 56° orientations; hence the airflow is less efficient in this region, as indicated in Figure 23.

Therefore, due to shock losses, the orientations of the drawbells affect the efficiency of the airflow in mitigating radon exposure. Since there is no notable airflow inside the cave, Figure 24 shows that the concentration inside the cave increases significantly. In this case, the maximum concentration is about 20 WL, though mine personnel is not usually exposed to the higher levels in these regions. This agrees with previous studies that the working level in abandoned mines or caves can be as high as 81 WL [24]. Therefore, without undercut ventilation, radon accumulates significantly within the cave.

Figure 24.

Radon concentration in a section of the cave without the undercut ventilation.

Without undercut ventilation, negative pressure on top of the cave might have a negative impact on the radon concentration in the production drift.

We studied the effect of maintaining a negative pressure on top of the cave without undercut ventilation. Figure 25a shows radon concentrations before imposing the negative pressure condition, and Figure 25b shows the concentrations after imposing the condition.

Figure 25.

(a) Radon concentration before imposing negative pressure; (b) radon concentration after imposing negative pressure condition.

The effectively imposed negative pressure condition reduces radon concentration in the drawbell; however, radon concentration increases toward the end of the production drifts. This is due to significant air loss through the porous drawbells to satisfy the condition imposed. Therefore, the magnitude of air flowing through the drifts decreases, and the radon concentration increases. Although in most cases, the cave is not as porous as the discrete model (47%), this scenario is possible for a very porous cave or drifts with one or two hang-ups close to the drift’s inlet. Hence, without the undercut ventilation, maintaining a negative pressure on top of the cave might have a negative impact on the radon concentration in the production drift. Therefore, mitigation measures should be appropriately investigated before implementation because the system might respond differently based on the mine condition. In addition, since there is no more undercut level, one can consider increasing the airflow through the drifts instead of imposing a negative pressure condition on top of the cave.

3.10 Finding 10: ventilation shutdown causes variation in radon concentration at the production drifts

In most underground mines with radon sources, the ventilation is continuous to ensure radon concentration is within the permissible levels. However, certain situations such as maintenance or mechanical malfunction could lead to the shutdown of the ventilation system. This study investigates the effects of shutting down the ventilation system for a period of time using the discrete model without the undercut ventilation. Figure 26a shows the radon concentration contours for the model after about 1 hour without ventilation.

Figure 26.

(a) Radon concentration contours after 1 hour of ventilation shutdown; (b) pressure distribution after 1 hour of ventilation shutdown.

The result shows significantly higher radon levels due to the pressure drop in the production drifts after the ventilation is shut down. It is observed that locations with a high radon concentration level vary based on the pressure distribution shown in Figure 26b. There is a significant pressure drop at the inlet for the first and second drift as the airflow stops. Therefore, radon concentration increases suddenly toward both inlets due to the pressure difference. However, for the third drift, the trend is different. The third drift develops the maximum pressure due to the distance between the inlet and the first source of pressure loss (drawbell). Hence, after shutting down the ventilation of a mine, the pressure around the inlet of the third drift is still high enough to keep radon concentration low. Therefore, in the event of a ventilation shutdown, there might be a considerable variation in radon concentration through the drifts.

3.11 Finding 11: radon daughter concentration is a function of the air quantity supplied to the production drift, the emanating power, and the porosity of the broken ore

A CFD study to investigate the effect of changing cave porosity (Ø), air quantity (Q), and radon emanating power (B) on radon daughter emissions from a cave in a block/panel cave mine was conducted [25] {Bhargava, 2019 #10}. The concentration of radon daughters was measured at the exit of the panel cave (458 m from the inlet) for production drifts 1, 3, 5, 7, and 9. Figure 27 shows the concentrations of radon daughters (on the same scale for ease of comparison) for 18.5 m³/s air quantity, for 21% cave porosity with emanating powers of 6, 90, and 180 pCi/m³s, respectively. A summary of average radon concentration in drift # 5 (458 m from the inlet) was given in Table 6; similar data were also collected for the other drifts. The results were analyzed using R statistical software to develop the relationship between radon daughter concentration (WL), porosity, emanating power, and quantity supplied to the production drifts. The relationships were summarized in Table 7, where B represents the emanating power, Q is the quantity supplied, and Ø represents the porosity of the cave.

Figure 27.

Radon daughter concentrations (WL) in production drifts with simulation time of 1000 s at Q = 18.5 m³/s, B = 6, 90, 180 pCi/m³s (top to bottom), Ø = 21%.

		B = 6 pCi/m^3s	B = 90 pCi/m^3s	B = 180 pCi/m^3s
Q (m^3/s)	Ø (%)	WL	WL	WL
18.5	21	0.009	0.142	0.283
23.1	21	0.007	0.097	0.194
27.7	21	0.005	0.071	0.142
32.3	21	0.004	0.055	0.100
18.5	28	0.010	0.153	0.306
23.1	28	0.007	0.105	0.211
27.7	28	0.005	0.078	0.155
32.3	28	0.004	0.060	0.120
18.5	42	0.013	0.197	0.394
23.1	42	0.009	0.138	0.276
27.7	42	0.007	0.104	0.207
32.3	42	0.005	0.082	0.163
18.5	56	0.018	0.270	0.540
23.1	56	0.013	0.193	0.387
27.7	56	0.010	0.148	0.310
32.3	56	0.008	0.119	0.237

Table 6.

Summary of average radon daughter concentrations in drift # 5.

Production drift	Radon daughter concentration (WL)
Drift #1	$\mathrm{WL}={\mathrm{e}}^{-1.12}{\mathrm{Q}}^{-1.86}{\varnothing }^{0.13}{\mathrm{B}}^{0.99}$
Drift #3	$\mathrm{WL}={\mathrm{e}}^{-0.71}{\mathrm{Q}}^{-1.64}{\varnothing }^{0.69}{\mathrm{B}}^{1.00}$
Drift #5	$\mathrm{WL}={\mathrm{e}}^{-0.68}{\mathrm{Q}}^{-1.62}{\varnothing }^{0.73}{\mathrm{B}}^{0.99}$
Drift #7	$\mathrm{WL}={\mathrm{e}}^{-0.78}{\mathrm{Q}}^{-1.67}{\varnothing }^{0.59}{\mathrm{B}}^{1.00}$
Drift #9	$\mathrm{WL}={\mathrm{e}}^{-1.28}{\mathrm{Q}}^{-1.83}{\varnothing }^{0.05}{\mathrm{B}}^{0.99}$

Table 7.

Equations developed for predicting radon daughter concentrations in the production drifts.

From Table 7, we can deduce the following equation [26]:

$$\mathrm{WL}\text{in Drift}\#\propto \frac{{\mathrm{\varphi }}^{\mathrm{a}}\mathrm{B}}{{\mathrm{Q}}^{\mathrm{b}}}$$E2

(where) $0.05\le \mathrm{a}\le 0.65\text{and}1.62\le \mathrm{b}\le 1.86$

From Eq. (2), we can infer that the radon daughter concentration is directly proportional to the emanating power and porosity of the broken ore (raised to power a) and inversely proportional to the quantity (raised to the power b) supplied to the production drift.

3.12 Finding 12: radon diffusivity depends on the fracture sets, fracture orientations, and rock’s engineering properties

We introduced the concept of diffusivity tensor in fractured rocks [27], similar to permeability tensor [28, 29, 30, 31]. We have developed a method for predicting the diffusivity tensor and fracture porosity for fractured rocks using a discrete fracture network (DFN) analysis. This method applies to fractured rocks and mines if data such as fracture sets, fracture orientations, and radon generation rates are available. The following are the observations/conclusions from this study—(1) the concept of diffusivity tensor has been developed and implemented; (2) each of the DFN models established a representative elementary volume (REV), but at different DFN scales due to differences in fracture length distribution which emphasizes that the short diffusion length of radon affects the DFN scale to establish a REV; (3) radon diffusivity for the fractured rock increases with an increase in fracture density due to increased porosity; (3) fracture porosity can be related with the diffusivity tensor and used to predict radon flux emanation; (4) radon diffuses at about an equal rate in both directions since, the principal and cross diffusivity are numerically close due to the consistent generation of radon within the rock mass and; (5) the value of radon generation significantly affects radon diffusivity; hence, for the prediction of radon emissions, the site-specific data should be considered.

In the case of a particular field study, discontinuity data from boreholes, rock cores, and scanlines can be processed to identify fracture sets and their orientations used to tune the stochastic model to suit site-specific in situ conditions better. Therefore, this model predicts radon flux from fractured rocks, and it is beneficial for predicting radon flux from the rocks that are not easily accessible for field measurements.

From this study, we found that—(1) the proposed model predicts radon flux from the fractured rocks; (2) the model can be applied to specific locations if the site data such as fracture sets, fracture orientations, and rock’s engineering properties are available; (3) the model is very sensitive to the advection velocity model, and aperture model implemented; (4) incorporating the effect of stress into the model shows more heterogeneity related to radon transport as observed from field studies; (5) an increase in fracture density increases radon flux, and an empirical power law relationship is found to relate both parameters; (6) the empirical relationship can be used with measured radon flux from field studies to predict the rock’s fracture density; (7) radon flux increases with increase in radon generation rate, but not as sensitive as the fracture density, hence, increase in fracture density of a rock sample with uniformly distributed radon generation rate (q) increases radon flux more than another rock sample with an equivalent increase in radon generation rate.

3.13 Finding 13: additional fan increased cave airflow resistance and decreased the exponent n value

This study developed a 1:100 scaled experimental model (Figures 28 and 29) to determine the effects of the change in porosity and particle size of caved materials, undercut structure, and additional fan operation on the cave airflow behavior by developing P-Q curves and equations [32, 33, 34, 35].

Figure 28.

Experimental model showing production drift inlets.

Figure 29.

Arrangement of drawbells and drawpoints with an El Teniente layout.

As shown in Figure 30, our scaled block cave model has a caved zone, production level, drawbells, undercut level, and multiple ducts connecting with two exhaust fans. Initially, the wood model had eight rigid windows, and the caved zone dimensions were 244 cm × 229 cm × 122 cm (width × length × height); while in our modified version, they are 244 cm × 229 cm × 81 cm without windows. The production level consists of nine parallel drifts with a cross-section of 5 cm × 5 cm, a center-to-center distance of 30 cm between the drifts, and 188 drawpoints with an El Teniente layout. The caved zone and the production level are connected by 94 drawbells (shown in yellow, red, and green colors). Three 5 cm diameter PVC pipes (shown in blue color in Figure 30) are attached to the caved zone to simulate three undercut drifts. A 10 cm diameter PVC duct (shown in cyan color) is connected to the production drift outlets, and it is fitted with a fan (bottom fan shown in magenta color) to pull the air through production drifts. Another 20 cm diameter steel duct (shown in cyan color) is connected to the cave top, and it is also fitted with another fan (top fan shown in magenta color) to pull the air through the caved zone. The caved zone was divided into three vertical regions: Region 1, Region 2, and Region 3. Nine production drift inlets are shown in white color, and three undercut drift inlets (shown in white color) can be sealed with duct tape or opened during the experiment.

Figure 30.

Schematic diagram of the experimental setup (unit: cm).

Four different conditions for the top fan to develop a single P-Q curve, three different settings for the bottom fan to check the effect of an additional fan, and two undercut structures (closure and opening) to explore the airflow behavior change [36]. Each data set was repeated to obtain two replications, and the average value was used for data analysis.

Table 8 summarizes the cave characteristics under various conditions in terms of P-Q equations (P = RQⁿ). Undercut drift openings escalated the value of exponent n from 1.55 to 1.78, while the increase of bottom fan power abated the value from 2.17 to 0.71 in the experimental results. Typically, the value of n is around 1.8 for uniform airflow distribution in a regular porous media, and it is 2 for turbulent airflow through mine openings. Thus, in this study, the value of n represented the combination of airflow through an irregular cave and mine openings.

Undercut drifts	Bottom fan condition	Experimental value n	Undercut drifts	Bottom fan condition	Experimental value n
Closed	No fan	2.17	Open	No fan	2.24
	Half-open	1.13		Half-open	1.45
	Full-open	0.71		Full-open	1.17
	Overall	1.55		Overall	1.78

Table 8.

Average values of exponent n.

As shown in Table 9, the effect of three undercut drift openings on the exponent n value was not noticeable, but the increment of top fan power lowered the value.

Undercut drifts	Top fan condition	Value n	Undercut drifts	Top fan condition	Value n
Closed	0	2.16	Open	0	2.07
	70	1.85		70	1.91
	90	1.80		90	1.85
	Overall	1.93		Overall	1.94

Table 9.

Average values of exponent n with the top fan.

3.14 Finding 14: regulators and cave footprint changes the cave resistance

This study investigated the effects of cave footprint, and regulators on the cave airflow resistance. Regulators were able to distribute airflow rates through the caved zone and extraction drifts. Bends and regulators made it challenging to obtain constant air velocity within the ducts.

Both the experimental and CFD simulation results demonstrated (Figure 31) that the increment of porosity and particle size in the caved materials increases the area available for flow within the cave (decreased airflow resistance) and increased airflow distribution percentage through the cave system under a given regulator combination. The shrinkage/reduction of cave footprint decreases the area available for flow within the cave (increased cave airflow resistance) significantly. The use of regulators increased the fan head pressure, decreased the overall airflow rate, and changed the distribution of airflow rates through the cave system and extraction drifts. The increase of airflow rate through the system is favorable for gas dilution regardless of the source locations; while the regulated airflow system might deteriorate gas dilution performance, especially when the source is located at the extraction level (Tables 10 and 11).

Figure 31.

P-Q curves under various regulator combinations (left) and cave footprints (right).

Combination	Up regulator	Down regulator
C1	100%	100%
C2	100%	50%
C3	25%	100%
C4	6.25%	100%

Table 10.

Regulator combinations.

Note: % is the ratio of the open area over the regulator’s cross-sectional area.

no—no undercut drifts; three—three undercut drifts.

Scenario	Drawbells (O—open)	Drawbells (C—closed)
S1	94	0
S2	72	22
S3	52	42
S4	21	73

Table 11.

Cave footprint combinations.

3.15 Finding 15: blast fumes dispersion in undercut and drawbell development

CFD techniques were used to investigate CO diffusion characteristics in two common cases in an immature cave system, assuming CO is contained within the blasting zone [37]. The first case is undercut blasting with multiple ventilation structures (Figure 32). This study aims to find contaminated areas, potentially affected zones in the system, and develop CO concentration curves with respect to time at certain positions, and investigate the effects of broken rock size, porosity, and entrapping percentage on the gas diffusion characteristics in the muckpile. The second case is drawbell blasting (Figure 33) to investigate fume distribution and find possible zones that are likely to be filled with high concentration CO. All findings and observations provide helpful information to understand CO diffusion characteristics in block caving mines.

Figure 32.

CO diffusion characteristics in undercut blasting.

Figure 33.

CO diffusion characteristics in drawbell blasting.

4. Conclusions

This study provides valuable information for designing and optimizing an effective ventilation system for panel/block cave mines. The key findings are listed below.

1. The analysis of the airflow patterns through the cave indicated that the size and intensity of the recirculation zones change with the change in airgap heights. CFD simulation results show that in the absence of undercut ventilation, radon concentrations in the production level were much lower than the radon concentrations observed when the undercut level was ventilated which can be attributed to the creation of a low-pressure region in the undercut level, the porous nature of the cave, and the air recirculation in the cave.

2. Rock mass strength (RM) increases the heights of the cave porosity zones decreases for both block and panel cave models. This is because the propagation rate decreases as rock mass strength increases. When the ore extraction rate was increased, the cave zone height increased. The mobilized zone porosity tends to be higher as it is the actively flowing region in the cave.

3. The numerical model of porosity assessment using a discontinuum approach successfully modeled change in porosity associated with the fragmented rock mass flow in a mature cave and it was found that during material extraction, the porosity changes relatively higher in Isolated Extraction than in the Isolated Draw Zone. The sensitivity analysis on particle size distribution concluded that fragmentation size affects cave porosity.

4. In a fully developed cave, the radon concentration is high without undercut level ventilation and high in production drift with undercut ventilation. With undercut ventilation, negative pressure on the cave top effectively reduces radon concentration through the production drifts.

5. Radon concentration in the drifts was studied with a discrete and continuum model. Empirical equations developed using both models suggested that different relationships are required for the drifts based on their configuration. The numerical study also indicated that the undercut ventilation increases radon concentration in the production drift by transporting radon generated within the cave into the drifts. Even though the airflow is almost uniform through the production drifts, the locations of the maximum concentration varied inside the drawbells. The result showed significantly higher radon levels due to the pressure drop in the production drifts after the ventilation is shut down. It is observed that locations with a high radon concentration level vary based on the pressure distribution.

6. Without the undercut ventilation, the cave has less effect on the production drift ventilation as the pressure drop model agrees with the turbulent model; and an increase in cave porosity decreases the drift’s resistance to airflow.

7. By statistical analysis of the numerical results, we inferred that the radon daughter concentration is directly proportional to the emanating power and porosity of the broken ore and inversely proportional to the quantity supplied to the production drift. Radon Diffusivity depends on the fracture sets, fracture orientations, and rock’s engineering properties.

8. From the experimental and CFD analyses, it was concluded that the reduction in porosity and particle size of caved materials elevated cave airflow resistance. At the same time, the existing undercut openings decreased the airflow rate through production drifts, increased the overall airflow through the cave system, reduced the overall cave resistance, and increased the exponent n value in the P-Q equation.

9. The use of an additional fan increased cave airflow resistance and decreased the exponent n value. The use of regulators increased the fan head pressure, reduced the overall airflow rate, and changed the distribution of airflow rates through the cave system and extraction drifts.

Acknowledgments

The authors acknowledge the financial support from the National Institute for Occupational Safety and Health (NIOSH) (200-2014-59613) for conducting this research.

Conflict of interest

The authors declare that they have no conflict of interest.