**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 m<sup>3</sup> /s air

**Figure 27.**

*Radon daughter concentrations (WL) in production drifts with simulation time of 1000 s at Q = 18.5 m<sup>3</sup> /s, B = 6, 90, 180 pCi/m3 s (top to bottom), Ø = 21%.*

quantity, for 21% cave porosity with emanating powers of 6, 90, and 180 pCi/m<sup>3</sup> s, 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.

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

$$\text{WL in Drift} \# \alpha \frac{\Phi^{\text{a}} \mathcal{B}}{\mathcal{Q}^{\text{b}}} \tag{2}$$

(where) 0*:*05≤a≤0*:*65 and 1*:*62≤b≤ 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 [28–31]. 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


**Table 6.** *Summary of average radon daughter concentrations in drift # 5.* *Block Cave Mine Ventilation: Research Findings DOI: http://dx.doi.org/10.5772/intechopen.104856*


**Table 7.**

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

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 [32–35].

**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.

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 *Block Cave Mine Ventilation: Research Findings DOI: http://dx.doi.org/10.5772/intechopen.104856*

**Figure 30.** *Schematic diagram of the experimental setup (unit: cm).*

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* = *RQ<sup>n</sup>* ). 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.

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.
