Thermal Environment Control at Deep Intelligent Coal Mines in China Based on Human Factors

Abstract

Mechanical cooling of the entire mining tunnel, widely used in deep coal mines, has a significant energy-intensive consumption, particularly for intelligent mining tunnels. Therefore, localized cooling would benefit the intelligent mining industry. Current studies on the temperature, relative humidity, and air velocity under localized cooling for working protection are still unclear. A modified predicted heat strain model that is appropriate for warm and humid conditions is presented in this article and calculated using MATLAB. Results reveal that air temperature was the primary factor affecting underground miners’ safety. Increasing air velocity would improve the working environment when the thermal humidity index is lower than 32. Reducing total working time and wet bulb temperature would benefit underground miners’ security. For the cooling of intelligent mining tunnels, the recommended air velocity would be 2 m/s, and the maximum wet bulb temperature would be 28 °C for the 6-h working period and 26 °C for the 8-h working period. Results would be beneficial to the cooling of intelligent mining in China.

1. Introduction

The Chinese mining industry is dominated by deep underground mining [1,2]. Intelligent mining technology based on automatic mining methods was developed to respond to excessive energy consumption, low productivity, and high safety risks associated with traditional mining in China. Over 900 fully automated coal extraction faces are currently in operation [3]. Intelligent mining entails fewer underground workers and a lower metabolic rate, as shown in

Table 1. Mining workers in a work shift.

Intelligent Mining Faces	Workers (People/8 h)
	Traditional Mining	Intelligent Mining
1306 mining face, Licun Coal mine	28	16
1001 mining face, Huangling 1# Coal mine	11	3
1008 mining face, Fumei Coal mine	12	5
5# Xiangshan Coal mine	25	10
214,201 mining face, Hanjiawan Coal mine	15	6
7302 mining face, Yanzhou Coal mine	25	7
74,104 mining face, Zhangshuanglou Coal mine	16	5

and

Table 2. Metabolic rate of the mining workers.

Occupations	Metabolic Rate (W/m2)	Traditional Mining	Intelligent Mining
Inspector	160	√	√
Driver	160	√	√
Maintenance engineer	160	√	√
Coal cleanup workers	300	√	×
Workers at the head and tail of coal-cutter	250	√	×
Workers for the hydraulic supports	250	√	×

.

Mechanical cooling systems have been most frequently used in the thermal environment due to deep mining [2,4]. However, cooling an entire tunnel would be a significant energy-intensive process, accounting for up to 25% of total electricity consumption [4]. Moreover, the reduction in the number of workers and decreased physical work intensity suggest that localized cooling is widespread in the intelligent mining industry. Consequently, the climatic conditions under localized cooling will be studied.

Nations worldwide have focused on controlling the thermal environment under deep mines, as shown in

Table 3. Thermal environment controlling measures.

Country	Environmental Conditions
China	When the DBT exceeds 30 °C, production must be stopped.
United States	Upper WBT of 30 °C for unimpaired performance on sedentary tasks and 28 °C for moderate levels of physical work.
Australia	WBT ≤ 28 °C
France	Synthesizing temperature < 28 °C
Germany	1. When DBT > 28 °C or ET > 25 °C ET of 25–29 °C lasts for 3 h a day, working time should not exceed 6 h; ET of 29–30 °C lasts for 2.5 h a day, working time should not exceed 5 h; People would not be allowed to work when ET exceeds 30 °C. 2. Staff members aged under 21 or over 50 would be prohibited from working at the environment with ET exceeding 29 °C.
Great Britain	When the ET exceeds 28 °C, the working time would be no longer than 1.5 h.
	Semi-mechanization working face: ET ≤ 27.2 °C; Mechanized working face: ET ≤ 28.3 °C ET ≤ 30.0 °C: light levels of physical work; ET ≤ 28.0 °C: moderate levels of physical work; ET ≤ 26.5 °C: heavy levels of physical work.
Poland	When DBT > 26 °C, the workload should be reduced by 4%; DBT > 28 °C, working time should be no longer than 6 h; DBT > 33 °C, only ambulance work is allowed.
India, Italy	DBT ≤ 32 °C
Japan	DBT ≤ 30 °C in the mining face and 31 °C for heading face.
South Africa	WBT ≤ 27.5 °C
Former Soviet Union	When RH ≥ 90%, the allowable DBT was 25 °C if air velocity was larger than 2 m/s and no more than 24 °C if the air velocity was smaller than 1 m/s.

. Vosloo et al. [5] reported that underground working areas require WBT below 27.5 °C. Twort et al. [6] determined that ET 28.8 °C was the upper climatic design limit. Belle et al. [7] recommended lowering WBT below 27 °C to reduce the risk of heat stroke. Han et al. [8] showed that when the relative humidity is reduced to 60%, even the DBT of 30 °C satisfies the workers in the entire tunnel. Rohanchi et al. [9] found that a velocity of 1–2 m/s is ideal for guaranteeing the workers’ thermal comfort. Haiqiao et al. [10] stated that reducing RH would improve thermal comfort in deep mines. Shugang et al. [11] reported that the DBT in deep mines could be extended up to 28 °C. Saunders et al. [12] presented that 33 °C and 60% RH would be acceptable. Paloma Lazaro et al. [13] proposed a modified PHS model for deep mines. Sasmito et al. [14] found that DBT significantly impacts temperature distribution throughout the tunnel. Xingxin et al. [15] suggest that miners are most comfortable when temperatures are below 27 °C, humidity is between 60% and 70%, and wind speeds are at least 0.5 m/s. Telebi et al. [16] found that increasing clothing insulation lowers the maximum exposure time. Kalkowsky et al. [17] measured miners’ heart rates and rectal temperatures. Jinggang et al. [18] determined that the critical tolerable temperature and relative humidity were 37 °C and 80%, respectively. Chao et al. [19] demonstrated that it is necessary to stop working under heavy and cumbersome loads under WBGT over 35 °C. Jiasong et al. [20] found that increasing air velocity (less than 2 m/s) would increase miners’ exposure. Chenqiu et al. [21] proposed modifying inputs to improve the prediction of the PHS model underground. Sunkpal et al. [22] determined that the optimal air velocity for thermal comfort was 1.5 m/s. Zhaoxiang et al. [23] presented a classification criterion for assessing underground airway heat hazards. Zijun et al. [24] found that the temperature of the surrounding rock is the most significant factor in the release of latent heat and sensible heat. Jiuzhu et al. [25] report that ventilation temperature influences DBT the most. Dingyi et al. [26] studied heat stress with nine evaluation indexes. Ji et al. [27] concluded that the maximum safe working time is 4 h at 32 °C and 90% RH. According to Qianming et al. [28], workers in deep mines should be protected from high temperatures by a DBT of 27 °C.

The experts have agreed that the thermal environment under the deep mine needs to be improved. Unfortunately, the thermal environment-controlling conditions proposed did not agree with each other due to inconsistent heat stress indicators. Considering the safety and health of mining workers, a modified PHS model for a warm and humid climate would be more suitable for evaluating underground mines during intelligent mining. This paper presents and calculates a modified PHS model for a warm and humid climate using the MATLAB program. The impact of environmental parameters on maximum exposure duration was then examined. Lastly, raise the parameters for localized cooling at Jiahe Coal Mine.

2. Materials and Methods

DBT, RH, clothing insulation, exposure duration, and metabolic rates determine the heat effect on a mining worker. Heat illness occurs when heat stress exceeds the resultant heat strain. As a result, it is becoming increasingly significant to assess underground environmental conditions according to the allowable heat stress for the security of miners.

Heat gains and losses by the human body are expressed in the heat balance equation [29]. Figure 1 and Equation (1) illustrate that heat storage (S), which accumulates in the body, is the metabolic rate’s (M) less adequate mechanical power (W), as well as heat exchanges due to conduction (K), convection (C), radiation (R) and evaporation (E).

$$S=M-W-K-C-R-E$$

(1)

People would be comfortable when S equals zero. When S > 0, heat accumulates in the body, leading to a rising rectal temperature and water loss. Rectal temperature and water loss have been calculated using the PHS model in ISO 7933. ISO 7933 suggests that the maximum rectal temperature for workers is 38 °C and the maximum water loss is 7.5% of body mass. Mining companies and experts agree that the PHS index is a handy tool for evaluating and managing occupational heat exposures [13]. Most coal miners suffer from high temperatures and high humidity working under mine tunnels, where DBT is over 30 °C and RH is close to 100% [2,10]. Enzymatic activity increases when the body is exposed to a 30–45 °C environment, and chemical reactions accelerate [30]. During sweating, some sweat is absorbed into the clothes, causing a change of the clothes’ insulation, and the evaporated one causes a change in evaporation heat resistance. ISO 7933 simplifies metabolic rate, clothing insulation, and evaporation heat resistance as constants, resulting in inaccurate predictions of human heat tolerance under deep hot and humid conditions. The schematic diagram can be found in Figure 2.

2.1. Metabolic Rate of the Miners under Hot and Humid Mine

Metabolism measures the energetic cost of muscular load and is used as an activity indicator because it converts chemical energy to mechanical and thermal energy. Enzymatic activity increases when the body is exposed to 30–45 °C, and chemical reactions accelerate [30,31]. As a result, the empirical values provided by ISO 8996 cannot be applied to hot and humid conditions. Xiaoli et al. [32] found that a maximum increase of 5 to 10 W/m² would be expected in the hot climate due to increased heart rate and sweating. Werner et al. [33] demonstrated that M is a function of time and local coordinates in three dimensions. Experiments collected from previous studies [34,35,36] in a warm and humid environment (heart rate, respiratory quotient, oxygen consumption rate, carbon dioxide production, etc.) were used to calculate the actual metabolic rate (Ma) in a hot and humid environment utilizing Equations (2)–(5). Comparing the Ma and ISO8996 (Mexp), we found that the ∆M should be 20–30%.

$$M=\frac{HR-H{R}_0}{RM}+M_0$$

(2)

$$RQ=\frac{{\dot{V}}_{{CO}_2}}{{\dot{V}}_{O_2}}$$

(3)

$$\mathrm{EE}=5.88\cdot (0.23\cdot \mathrm{RQ}+0.77)$$

(4)

$$M=EE\times {\dot{V}}_{O_2}\times \frac{1}{A_{Du}}$$

(5)

$$\Delta M=\frac{M_{\mathrm{a}}-M_{\exp }}{M_{\exp }}$$

(6)

• M₀—metabolic rate at rest, W/m²;
• HR—heart rate, in beats per minute;
• HR₀—heart rate, in beats per minute;
• RM—increase in heart rate per unit of metabolic rate;
• RQ—respiratory quotient;
• ${\dot{V}}_{O_2}$—oxygen consumption rate, L/h;
• ${\dot{V}}_{{CO}_2}$—carbon dioxide production, L/h;
• A_Du—body surface area, m².

2.2. Modified Clothing Insulation (Icl) and Evaporation HEAT resistance (R_t)

Thermal balance is achieved by transferring sweat from the skin surface to the air in liquid and gaseous forms. Sweat is absorbed in clothes as a liquid; then, the insulation changes, and some evaporates, altering the evaporation heat resistance. Accordingly, we proposed:

(1)

No sweat remains on the skin’s surface;

(2)

Fabric volume was constant;

(3)

Heat conduction occurs when heat is transferred from the inside to the outside fabric;

(4)

The clothing fabric is made of 100% cotton.

Cotton fiber diameter increases when cotton fibers absorb water. Moisture regain is calculated when it exceeds the maximum water capacity (Cm).

$$\epsilon =1-\frac{{\rho }_t}{{\rho }_s}$$

(7)

$$C_m=\frac{{\rho }_w}{{\rho }_s}\frac{{\epsilon }_d}{1-{\epsilon }_d}$$

(8)

where ρ_t, ρ_w and ρ_s, respectively, indicated the density of fabric, water and fiber, g/cm³. εd represents the porosity factor when the clothing was completely dry.

As the fabric is porous, heat transfer in the clothing (Q) is divided into conduction through the air in the pores (Qa), sweat in the pores (Qw), and fibers (Qs).

$$Q=Q_a+Q_w+Q_s$$

(9)

Fourier’s law states:

$$Q_j=\frac{{\lambda }_j{A}_j\Delta t}{\delta }$$

(10)

where subscript j represents air, fiber and sweat; λ, λ_s, λ_a and λ_w, respectively, indicate the thermal conductivity of clothing, fiber, air and water, J/m∙ °C. ∆t was the temperature difference between the inside and outside of the clothing, °C. δ was the thickness of the clothing.

If water mass (m_w) ratio to fiber mass (m_s) in clothing is less than C_m, that is, $\frac{m_w}{m_s}<C_m$, water volume fraction in the fabric was supposed as x; otherwise, the moisture content of the clothing (μ) would be a constant value C_m.

Total weight of the clothing at the i moment (m_i) was expressed as in Equation (11).

$$m_{wi}+m_s=m_i$$

(11)

$$m_{i+1}=\left(sw{t}_{i+1}-m_{zi}-m_{si}\right)\cdot \Delta t+m_i$$

(12)

$$sw{t}_i=m_{zi}+m_{si}+m_{li}$$

(13)

where,

• m_i—Total weight of the clothing at the i moment, kg;
• swt_i—Total sweat at the i moment, kg/s;
• m_zi—Sweat that enters the air as a gas through a fabric, kg/s;
• m_si—Sweat evaporating from the outer surface of the garment, kg/s;
• m_li—Sweat remaining in the fabric;
• ∆t—Iteration interval, 60 s.

$$m_{wi}=\mu m_i$$

(14)

The simultaneous Equations (9)–(14) were derived and the following results were obtained:

$$\lambda =(1-\epsilon ){\lambda }_s+(\epsilon -x){\lambda }_a+x{\lambda }_w$$

(15)

$$x=\frac{\mu (1-\epsilon ){\rho }_s}{{\rho }_w(1-\mu )}$$

(16)

Equation (17) specifies the clothing insulation (Icl), whereas Icl0 refers to the initial clothing insulation, Clo.

$$I_{\mathrm{cl}}=0.155\cdot \frac{\delta }{\lambda }$$

(17)

$$I_{cl0}=0.161+0.835{\displaystyle \sum I_{clu0}}$$

(18)

ISO 9920 suggests mining clothing parameters in

Table 4. Mining clothing parameters.

Material	Fabric Insulation (m2∙KW−1)	Fabric Surface Density (g/m2)	Thickness/mm
Denim/twill weave	0.023	206	0.8

. Iclu0 of mining cloth was given in

Table 5. Insulation for typical clothing ensembles (ISO 9920).

Clothing Ensemble	Iclu/clo
Briefs	0.04
Long sleeves	0.16
Work pants	0.24
Socks	0.02
shoes	0.02
Cap	0.01

, and the Icl0 calculated as in Equation (18) was 0.57 clo. Figure 3 shows the results of several studies that have verified the model’s reliability [37,38].

$$I_{cl}=I_{cl0}\cdot \left(0.41+0.29e^{-\frac{m_{li}}{885.73}}+0.31\cdot e^{-\frac{m_{li}}{162.88}}\right)$$

(19)

According to the assumption, sweat equals the mass of water remaining in the clothing plus water evaporated from the skin and outer surface of the dress. As a consequence of the mass transfer process and the ideal gas state equation, m_zi would be expressed as follows.

$$m_{zi}=\frac{h_m}{R_w\cdot T_{cl}}\left(P_a-P_{cl}\right)\cdot A_i=\frac{\left(P_{cl}-P_{sk}\right)}{R_{zi}}$$

(20)

$$\begin{array}{ll}{R}_{zi}& =\frac{R_w\cdot T_{sk}}{h_m\cdot A_i}\cdot \frac{\left(P_{cl}-P_{sk}\right)}{\left(P_a-P_{cl}\right)}\\ & =\frac{R_w\cdot T_{sk}}{h_m}\cdot \frac{\delta }{{\epsilon }_i\cdot V}\cdot \frac{\left(P_{cl}-P_{sk}\right)}{\left(P_a-P_{cl}\right)}\end{array}$$

(21)

$$R_{ti}=\frac{R_{zi}}{r}$$

(22)

and because of

$${\epsilon }_i={\epsilon }_0-x_i$$

(23)

Then,

$$m_{si}=\frac{h_m}{R_w\cdot T_{cl}}\left(P_a-P_{cl}\right)\cdot \left(\frac{V}{\delta }-A_i\right)$$

(24)

Imagine the human body as a cylindrical shape; the Re, Sc were calculated using Equations (25) and (26).

$$h_m=\frac{Sh\cdot D_a}{d}$$

(25)

$$Sh=0.26{\mathrm{Re}}^{0.6}S{c}^{0.38}{(\frac{S{c}_{\infty }}{S{c}_{cl}})}^{0.25}$$

(26)

where

• h_m—Mass transfer coefficient, m/s;
• T_cl—Water vapor saturation temperature of a garment’s outer surface, K;
• R_w—Constant of water vapor gas, 461.89 J/(kg·K);
• P_cl—Water vapor pressure on the outer surface of the garment, Pa;
• Pa—Water vapor pressure in roadway air, Pa;
• Psk—Water vapor pressure on the surface of the skin, Pa;
• Ai—effective evaporation area, m²;
• R_zi—Moisture vapor resistance at i moment, Pa·s/kg;
• R_ti—Evaporative resistance of clothing at i moment, kPa·m²/W;
• r—latent heat of vaporization, J/kg;
• d—the diameter of the bottom surface, 0.3 m;
• D₀—Coefficient of diffusion, cm²/s; u-air velocity, m/s.

2.3. Model Verification

Previous experiments have validated the modified PHS model [39,40,41]. The experiment results and predictions are listed in

Table 6. Model validation with study of Shapiro et al. [39].

Clothing	Walking Speed (m/s)	Treadmill Grade (%)	Sweat Loss (g/(m2·h))	Predicted Sweat Loss (g/(m2·h))	Error Rate (%)
Fatigue	Rest	-	198 ± 15	193.94	2.05%
Fatigue	1.34	0	580 ± 31	540.44	6.82%
Fatigue	1.34	5	691 ± 41	644.76	6.69%
Shorts	Rest	-	164 ± 16	156.69	4.46%
Shorts	1.34	0	386 ± 43	415.89	7.74%
Shorts	1.34	5	556 ± 23	594.58	6.94%

,

Table 7. Model validation with study of Qingqing et al. [40].

	Clothing	Walking Speed (m/s)	Sweat Loss (g/(m2·h))	Predicted Sweat Loss (g/(m2·h))	Error Rate (%)
Still conditions	0.56	0	28	30.45	8.76%
	0.6	0	35	38.45	9.86%
	0.68	0	50	50.65	1.29%
Activity 1	0.56	0.8	38	41.69	9.72%
	0.6	0.8	43	43.04	0.10%
	0.68	0.8	52	54.62	5.04%
Activity 2	0.56	1.2	53	57.69	8.86%
	0.6	1.2	59	58.96	0.06%
	0.68	1.2	70	69.60	0.58%

and

Table 8. Model validation with study of Mehnert et al. [41].

Parameters	Experiment 1	Experiment 2
Number of subjects	58	56
Ta (°C)	32	25
RH (%)	50–55	50–55
Icl	0.6	0.85
Total sweat loss (g/m2)	261	232
Predicted sweat loss (g/m2)	264	221
Error rate (%)	1.2%	4.7%

. The error rate between the experiment results and predictions was significantly lower than 10%, implying an acceptable amount of error in the theoretical calculation.

An experiment by Shapiro et al. [39] involved 34 male soldiers dressed in T-shirts, shorts, socks, and indoor shoes. The average age of volunteers was 22.1 years old, weighing 71.3 kg and 176.4 cm tall. Each exposure lasted 120 min: 10 min of walking, followed by 10 min of rest, followed by a 50-minute walk or 120 min of continuous rest for the resting group. The ambient temperature was 35 °C, and the relative humidity was 75%.

Twenty college students (mean values: age: 23.5 years; height: 167.7 cm; weight: 57.6 kg) were recruited for the experiments of Qingqing et al. [40]. The maximum metabolic rates of activities 1 and 2 were, respectively, 1.8 and 2.6 met. DBT was 30 °C and RH was 52% during the investigation.

According to Mehnert et al. [41], subjects sat in a wire chair in a reclining position in experiment 1 and a standard car seat with a four-point seat belt in experiment 2.

3. Results and Discussion

The modified PHS model was calculated using MATLAB, and the parameters are listed in

Table 9. Calculating parameters.

Calculating Parameters	Values
Weight/kg	65
Height/m	1.72
Mexp/W/m2	160
Icl0/clo	0.57
DBT/°C	16–50
RH/%	60–95
Air speed u/m/s	1–4
Walking Speed Wa/m/s	1
Duration/minutes	480

. Resting times would affect the maximum exposure duration and cooling parameters. However, the resting time at the coal mines we considered for cooling, for example, the Zhangshuanglou Coal Mine [8], Jiahe Coal Mine, and Zhangxiaolou Coal Mine [42], was irregular. People would take a break between jobs or when they felt fatigue. Moreover, we investigated additional coal mines in China for resting time and found the same results, as shown in

Table 10. Resting time of underground mining workers at coal mines in China.

Coal Mine	Work Shift	Resting Time/Minutes
Hongyang 3# Coal Mine	Three eight-hour shifts	30
Dongqu Coal Mine	Four six-hour shifts	0
Jinggong Coal Mine	Three eight-hour shifts	20–30
Longwanggou Coal Mine	Three eight-hour shifts	30
Liuyuanzi Coal Mine	Three eight-hour shifts	≤25
Hecaogou Coal Mine	Three eight-hour shifts	30
Zhangshuanglou Coal Mine	Three eight-hour shifts	almost 30
Jiahe Coal Mine	Three eight-hour shifts	almost 30
Zhangxiaolou Coal Mine	Three eight-hour shifts	almost 30
Sanhejian Coal Mine	Three eight-hour shifts	almost 30
Zhangji Coal Mine	Three eight-hour shifts	almost 30
Zhouyuanshan Coal Mine	Three eight-hour shifts	almost 30

. As a result, resting time should have been included in our article.

3.1. Exposure Duration of Miners in the Hot and Humid Mine

Thermal humidity index (THI) [43] is used for the evalution of the thermal environment.

$$THI=T_a-0.55\cdot \left(1-RH\right)\cdot \left(T_a-14.5\right)$$

(27)

When the THI is more than 32 (Figure 4(5),(6)), S will increase, and the critical physiological index will shortly be reached; therefore, workers will not be able to have heat blown away by increasing wind speeds. When the THI is lower than 32, increasing air velocity will improve the working environment, but the action becomes insignificant when the wind speed increases above 2 m/s. Moreover, our previous study [44] estimated that an air speed of 2 m/s would be the most negligible wind speed capable of dispersing the haze. Consequently, a 2 m/s air velocity should be recommended while localized cooling occurs in the tunnel.

DBT and RH have a negative impact on exposure duration, as seen in Figure 4. A variance analysis was conducted using SPSS22.0 software to assess the effect of environmental factors on exposure duration. Exposure duration was analyzed as a dependent variable, while environmental conditions were analyzed as an independent variable. Results in

Table 11. Variance analysis of the effects of environmental factors.

Source	Type III Sum of Squares	Degrees of Freedom	Mean Square	F	Salience
Corrected model	5197	20	259.8	230	0.000
Intercept	29,256	1	29,256	25,921	0.000
DBT	4816	6	802.7	713.5	0.000
RH	625	4	156.3	139.5	0.000
Wind speed	798	5	159.6	145.4	0.000
Error	995	911	1.1
Total	41,687	895
Corrected total	7145	893

indicate that exposure duration is most affected by DBT.

Green and pink shaded areas in Figure 4 indicate the comfort temperature zone and the safety zone at 2 m/s, respectively. In Figure 4, it appears that RH has little impact on the size of the tolerance zone and comfort zone when RH is above 70%.

3.2. Economic Analysis of Localized Cooling under Deep Mine

Using Jiahe Mine’s 9435 working face as an example, as seen in Figure 5, the localized cooling of working faces was evaluated for economic feasibility. Located at a depth of −1000 m, the 9435 working face with a tunnel perimeter of 8.6 m has a rock temperature of 34 °C and an inlet DBT of 37.4 °C. The thermoregulation device is an air–water heat exchanger installed on one side of the tunnel, cooling the working area. According to Figure 6, hot and humid air (point W) partially enters the thermoregulation device and is refrigerated by cold water circulating in the pipes. The cooled air (point O) mixes with the hot and humid air (point N), with N being the appropriate state for workers.

Six miners worked in a tunnel measuring 300 m in length that required cooling in this area. The operating cost would be predicted by Equations (27) and (28). Calculations of sensible and latent heat are presented in

Table 12. Sensible heat loads when the DBT is 26~31 °C and RH is 80%.

DBT (°C)	Equipment Load (W)	Staff Load (kW)	Ventilation Load (kW)	Surrounding Rock Heat Dissipation (W)	Total Sensible Heat Load (kW)
26	1200	1.2	253.10	11,022.88	264.57
27	1200	1.2	230.90	9795.02	241.30
28	1200	1.2	208.70	8567.16	217.63
29	1200	1.2	186.49	7339.30	194.20
30	1200	1.2	164.29	6111.44	170.77
31	1200	1.2	142.09	4883.58	147.34

and

Table 13. Latent heat loads when DBT is 29 °C.

RH (%)	Personnel Loose Moisture (W)	Ventilation Latent Heat Load (kW)	Moisture Loss of Surrounding Rock (kW)	Total Latent Heat Load (kW)
60	0.26	129.61	3.97	136.51
65	0.26	113.73	3.47	119.80
70	0.26	97.76	2.98	103.00
75	0.26	81.70	2.48	86.10
80	0.26	65.54	1.98	69.11
85	0.26	49.29	1.49	52.03
90	0.26	32.94	0.99	34.86
95	0.26	16.50	0.50	17.58

.

$$E_w=\frac{L_{od}}{CO{P}_R}$$

(28)

• $E_w$—Theoretical power consumption of refrigeration unit, kW;
• $L_{od}$—Total sensible heat load of the system, kW;
• $CO{P}_R$—Coefficient of performance of refrigerator, multi-purpose screw refrigerator, take 4.7;

Then, obtain the annual cooling cost per ton of coal according to the electricity fee.

$$F_w=\frac{n\cdot E_w\cdot F_d}{D_t}$$

(29)

• $F_w$—Annual cooling cost per ton of coal, RMB/t;
• $F_d$—electricity price, RMB/kW·h;
• $n$—annual operating time, h;
• $D_t$—The annual coal mining volume of the working face, t;

Figure 7 and Figure 8 show that the cooling operation cost and the maximum WBT for the 6- and 8-hour working periods can be calculated. The article suggested that the cost and WBT would have changed little when the air velocity exceeded 2 m/s. Consequently, the recommendation for air velocity would be 2 m/s, with the maximum WBT being 28 °C for six hours and 26 °C for eight hours.

4. Conclusions

Due to deep mining, mechanical cooling systems have been most frequently used to control thermal environments. However, cooling an entire mining tunnel would be a significant energy-intensive process. A reduction in workers and a decline in physical work intensity indicate that localized cooling will be widespread in the future intelligent mining industry. Therefore, the climatic conditions under localized cooling will be studied to protect mining workers. A modified PHS model for a warm and humid climate was presented in the article, along with reasonable and secure environmental parameters. Furthermore, the results are listed as follows.

(1)

The article modified the PHS model to account for warm and humid conditions based on human factors. The metabolic rate, clothing insulation, and evaporation heat resistance of clothing were modified for warm and humid underground environments. Based on the modified PHS model, the duration of miners’ exposure was calculated using MATLAB.

(2)

Air temperature was the primary factor affecting underground miners’ safety, followed by relative humidity and air velocity. The improvement of the thermal environment by increasing air velocity is directly related to the thermal humidity index; when the thermal humidity index is lower than 32, increased air velocity will achieve a significant cooling effect.

(3)

The recommended air velocity would be 2 m/s, and the maximum temperature of the wet bulb would be 28 °C for a 6-hour working period and 26 °C for an 8-hour working period, taking into account the security of mining workers and the economic efficiency of the cooling system.

This study may contribute to the cooling of intelligent mining and the formulation of cooling standards in China.