The Constructive Role of Ventilation Models in Atmospheric Monitoring Signal Evaluation

"It is prudent to interpret atmospheric monitoring signals in real-time for checking the safe limits of the air conditions in underground mines. In gassy mines, such real-time evaluation increases the safety of operation. In all mines, the continuous monitoring and evaluation contribute to maintaining air conditions to be within healthy and safe limits. Signal interpretation for safety conditions in mines is difficult for many reasons. 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 paper focuses on analyzing signal patterns to recognize dangerous trends as well as forward predicting the various environmental and working conditions that may affect safety in underground mines. Efficient numerical ventilation model with heat and gas contaminant simulation components is needed for the analysis of real-time atmospheric monitoring data. Computational speed enhancement methods are discussed and tested in numerical examples, using the Jacobian sensitivity matrices for signal propagation prediction. INTRODUCTION The focus area of this paper is mine safety, related to hazardous atmospheric conditions in a mine ventilation system. Improvements by recognizing safety hazards caused by heat of combustible gases or the accumulation of poisonous gases in a mine are addressed. The recognition of safety hazard by manual evaluation is difficult because of the complex nature of information and the large amount of monitored data from measurement by atmospheric sensors such as air velocity, pressure, hazardous gas contaminants and temperature. In order to recognize problem-causing trends, it is necessary to evaluate continuously individual measurement data from the Atmospheric Monitoring System (AMS) of the mine during their time-dependent variations. In addition, the combined effects of various signal trends need to be simultaneously interpreted in their cross-effects. It is very difficult to foresee the possible outcomes of intertwined signatures of various problems by continuous human observation. For example, a steady, continuous methane concentration measurement together with a sharp drop in barometric pressure from the AMS sensors may be a case for worry in future time due to pressure-induced methane increase from the coal seam or the gob (Danko et al., 2013). This future increase can be predicted by a time-dependent Ventilation Air Model (VAM) and its surrogate Air Parameter Predictor Solver (APPS) model. A calibrated VAM or APPS model can indeed predict a likely methane concentration variation in future time as a function of previous and new air pressure as well as air velocity (Danko, et. al., 2013). Such a future trend, however, cannot be seen from the raw measurement data."