Dynamic atmospheric signal analysis for improving mine safety and health

Abstract

There are a number of contaminants generated from strata and equipment usage in underground mines including poisonous and combustible gases, as well as heat. Mine ventilation is utilized to dilute the gases and cool the mine to provide conducive environment for mine workers. In order to ensure that contaminant levels are within acceptable regulatory limits, various sensors are installed in strategic places in the mine for monitoring.Continuous atmospheric monitoring is one of the tools used to achieve health and safety limit compliance and to ensure the quality of air conditions in underground mines. It is challenging to interpret monitoring sensor signal for accident prevention due to different contributing factors. The possibility of contaminant accumulation can be dangerously high as the concentration pulse traveling in the air moves from one location to another. This can be attributed to the inherent delay processes associated with the concentration pulse as it travels with the air velocity. As such, the identification of the delay hazard processes is of prime importance in predicting and preventing any future contaminant concentration increase in the traveling front.An increase of hazardous contaminant concentrations can be predicted by signal pattern recognition, root-cause analysis of rapid changes toward deterioration and forward prediction in time using algorithms and numerical models. This study focuses on analyzing signal patterns to recognize dangerous trends due to delayed processes by predicting contaminant concentrations for safety checking in underground mines. Efficient numerical ventilation model with contaminant simulation components is needed for the analysis of real-time atmospheric monitoring data. Examples of signal analysis and forward prediction of concentration are demonstrated in mine examples and the new results are presented for the application to improve mine safety and health.