No abstract

As safety at work has become a number one priority in every industry, it is no wonder that new technology and ideas are being tested to improve safety in a variety of workplaces, including, surface and underground mining, construction sites, power plants, factories, etc. Sensor technologies embedded in personal protective equipment (PPE), can be employed to monitor workers' health, exposure to harmful elements, their proximity to danger zones, etc. This technology connected to smartphones and smartwatches can also heighten workers' awareness by collecting data on the workplace itself, detecting environmental and health hazards. In this paper, we will present a prototype system that can be employed in underground mining which uses sensors attached to regular PPE clothing, including hard hats and safety glasses which are connected to smartphones and smartwatches via energy-effi cient Bluetooth sensors, to provide real-time safety situation awareness and predict health and safety incidents before they occur.

Unsafe working conditions in underground mines have led to a number of accidents, loss of life, damage to property, interruption of production, etc. Safety is essential in mining industry, which in recent years mainly focuses on injury prevention in the workplace through a variety of procedures and employee training. The primary goal of this paper is to present a methodology with systematic analysis to determine the most risky places for fire occurrence in underground mines and using a computer simulation to determine the movement of smoke and fire gases trough underground mining facilities from which depends the safe evacuation of all employees.

In the process of designing a fire safety system for underground mines, computer fire models can be used to analyse and estimate the consequences of fire scenarios for the evacuation process and the safety of mineworkers. The models need to be fed with data, some of which is stochastic in nature. Recent literature addresses the need for a computationally effective methodology for introducing uncertainties in the input parameters of fire and evacuation models to improve safety in underground mines. This research paper presents the results obtained from a methodology that implements Monte Carlo simulation, which follows the normal distribution of the fire load and the pre-movement time uncertainty to generate multiple scenarios that are simulated in a 3D model to show the propagation of combustion products through the mine ventilation network. These results are then used to estimate the fractional effective dose (FED) of fire combustion products in workers, and the available safe egress time (ASET) and required safe egress time (RSET), which can highlight the safety issues in the evacuation process. To demonstrate the model, a case study of the SASA-R.N. Macedonia lead-zinc mine was used in which 50 variations of scenarios were simulated. The results from the simulations are analysed and potentially harmful fire scenarios highlighted. In addition to being able to identify potentially dangerous fire scenarios, the model can also help in the process of conducting fire risk assessment and in improving the evacuation system in the case of an underground mine fire.

The total operating costs of each mine largely depend on the method of mining. Therefore, the appropriate choice of the method of mining excavation is very important and great attention is paid to this issue. There are several procedures for the selection of the mining method, among which the most important and most commonly used numerical method is the UBC methodology for the selection of the mining method. According to this methodology, the choice of the method of mining excavation is based on the mining-geological parameters of the ore and adjacent rocks. In this paper, the UBC methodology for the selection of the mining excavation method for a specific case will be applied. According to this methodology, it was obtained that Cut and Fill method is best ranked for specific conditions and the most appropriate way of mining.