Dynamic Models in Atmospheric Monitoring Signal Evaluation for Safety, Health and Cost Benefits

Abstract

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, real-time evaluation increases the safety of operations. In all mines, continuous monitoring and evaluation contributes to maintaining air conditions within healthy and safe limits. Signal interpretation for safety conditions in mines is difficult for many reasons. An increase in 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. The paper describes an early warning system for analyzing monitored signal patterns continuously in real time as well as forward predicting the various environmental and working conditions to recognize dangerous trends that may affect safety and health in underground mines. A dynamic, numerical ventilation model with heat and gas contaminant simulation components is used for the analysis of atmospheric data. Methods and test results are discussed with numerical examples for signal propagation prediction. Several mine examples are studied using controlled, synthetic data for malfunction simulations to evaluate time delays between the detection time of suspicious signal trends and the time of dangerous threshold crossing marking an accident scenario. Delay time is found in the order of 20 min in the examples, signifying the useful time period for preventive intervention between EWS warning and the likely breakout of a following accident.