# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 1 Introduction  
The environmental conditions in an underground mine are crucial in terms of efficiency and health and safety conditions [1; 2]. Furthermore, these factors have to be taken into account for mine planning due to the restrictions imposed by proper ventilation conditions. This becomes increasingly relevant as shallow deposits run out and mineral resources are extracted at greater depth, and as mines increase in size with more infrastructure, with an intensification in the use of mining equipment [3; 4] and human resources [5]. This creates a situation where the potential effects must be defined [6], with a need to determine the best possible conditions related to performance in the workplace [7; 8; 9; 10].  
One of the most important variables regarding environmental conditions in an underground mine is temperature [11; 12; 13]. It not only affects the concentration levels of the workers [14], but also creates unsafe conditions [15; 16]. Currently, there is an increasing interest in the characteristics of underground spaces [17], with previous studies creating a model to predict the temperature at the bottoms of shafts in metal mines [18], simulating the temperature at the working face of a coal mine [19], and determining the main parameters of heat from the intrinsic properties of the deposit [20]. In this regard, airflow is crucial to maintain adequate temperatures in the workplace, ensuring its supply during the whole lifespan of the mine and any future changes in the ventilation circuit [21].  
There were even some studies focusing on potash mining [3] or applying cooling systems [22; 23; 24]. However, it is necessary to not only identify the current conditions in terms of temperature, but also to predict the future conditions in order to plan the necessary actions. Some recent studies focusedon obtaining an equation to determine temperature at working faces [25], or determine the most efficient airflow velocity for thermal comfort in underground mines [18]; however, there is still a lack of knowledge pertaining to other types of mining activities with regard to identifying the temperature in the main ventilation system, as well as developing a system able to test different possible scenarios in the future development of the mine.  
Knowledge of the temperature in the drifts is crucial to understanding the future conditions of the workplace and working faces and, therefore, correctly planning the airflow required over time [26], and improving the main ventilation system. There are many factors that contribute to increasing the temperature; among them, some of the most important are equipment, autocompression, and geothermal gradient [27]. However, each mine has its own weaknesses and constraints, whether related to the deposit or otherwise.  
Many modeling methods have been proposed to develop predictive models in the engineering field in recent years [28]. Multivariate regression analysis is one of the methods most commonly used [29; 30], whereas other techniques such as artificial neural network and support vector regression are also implemented [31]. A mathematical model for underground coal mining was proposed by [32], while an initial model was also proposed for a deep gold mine [33]; however, they were validated with only a few temperature measurements. The intrinsic characteristics and high variability of the environmental conditions in an underground mine require the use of a large database recorded over a long period of time to validate any model proposed.  
The aim of this research was to find a model able to predict temperature in the main ventilation system from a case study, which was also easy to implement in any mine and was versatile depending on the data available and requirements. Actual data collected from the main ventilation circuit over three and a half years were used.