## 1 Introduction  
Unlike hazards of gas, fire, water, and roof, thermal hazards, which are considered non-fatal and tolerable for underground workers, have not been focused on for a long time. However, this issue has become an unavoidable global issue with deep-mining and occupational health system improvement [1; 2; 3]. There are up to 47 deep mines with a depth over one kilometer in China, such as Suncun Coal Mine (1500 m) and Jiahe Coal Mine (1200 m), all of which have reached the criteria for Grade III thermal hazard (airflow temperature \(\geq\) 32 \({}^{\circ}\)C) [4; 5; 6]. Low-level thermal hazards can be alleviated by properly increasing the airflow speed. However, mines with severe thermal hazards must be equipped with air-conditioning cooling systems (e.g., air cooling, water cooling [7], and HEMS cooling [8]). Mine intelligent ventilation (MIV) is an important safeguard for intelligent mining, and its goal for hot mines is to achieve airflow and cooling regulation according to the needs for mine climate condition improvement and disaster relief [9]. To accommodate this goal, firstly, the layout of sensors should be optimized, including air velocity and pressure sensors, airflow temperature and humidity sensors, and fan operation monitoring sensors, to conduct data intelligence and processing. In particular, the evaluation of the thermal comfort based on the underground microclimate is of necessity. Secondly, a real-time network solution should be provided to accurately identify abnormality and give prewarning (e.g., gas, dust, heat). It will take a certain time from the above visualization to regulation, which is a gray time period with hidden safety trouble, so it can be seen that such regulation has some lag. For this reason, distributing air volume and cold capacity according to the needs predicted in advance is the key link of intelligent decision making. Finally, coordinated control of equipment is realized to carry out automatic regulation of the flow resistance and the working conditions of air-conditioning and mine ventilators. Therefore, predicting airflow humidity and temperature in the roadway conduces to intelligent control of air-conditioning cooling and ventilating regulation, and finally serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control under the background of deep mining and MIV. In respect of heat exchange, the airflow temperature rise is mainly from the heat dissipated by the surrounding rocks [10], especially when the airflow is transported through a long distance from the pit bottom to the working face (Figure 1). The heat is exchanged between airflow and surrounding rock unsteadily, in a complex manner [11], and its theoretical calculation is based on the unstable heat exchange coefficient \(\rm K_{\tau}\) derived from the concept of the temperature regulation sphere. Many scholars have established methods to find the solution of \(\rm K_{\tau}\), including the accurate solution method [12; 13], the Laplace transform method [14; 15], the variable separation method [16], and the numerical solution method [17; 18], but the solution finding processes are relatively complex. Because of the non-linearity of the differential equations of heat conduction, these methods have poor usability. For this reason, some scholars have proposed empirical equations for calculation of the airflow temperature based on the statistics and fuzzy mathematics, etc. [19; 20]. However, each of these equations is typically a multivariate function, so when the application scope is beyond the statistical scope, the error will be relatively large.