In the coal mining industry, explosions or mine fires present the most hazardous safety threats for coal miners or mine rescue members. Hence, the determination of the mine atmosphere explosibility and its evolution are critical for the success of mine rescues or controlling the severity of a mine accident. However, although there are numbers of methods which can be used to identify the explosibility, none of them could well indicate the change to the explosion risk time evolution. The reason is that the underground sealed atmospheric compositions are so complicated and their dynamical changes are also affected by various influence factors. There is no one method that could well handle all such considerations. Therefore, accurately knowing the mine atmospheric status is still a complicated problem for mining engineers. Method of analyzing the explosion safety margin for an underground sealed atmosphere is urgently desired. This article is going to propose a series of theoretical explosion risk assessment models to fully analyze the evolution of explosion risk in an underground mine atmosphere. Models are based on characteristics of the Coward explosibility diagram with combining mathematical analyzing approaches to address following problems: (1) for an “not-explosive” atmosphere, judging the evolution of explosion risk and estimating the change-of-state time span from “not-explosive” to “explosive” and (2) for an “explosive” atmosphere, estimating the “critical” time span of moving out of explosive zone and stating the best risk mitigation strategy. Such research efforts could not only help mine operators understand the explosibility risk of a sealed mine atmosphere but also provide a useful tool to wisely control explosive atmosphere away from any dangers. In order to demonstrate research findings, case studies for derived models are shown and are also used to instruct readers how to apply them. The results provide useful information for effectively controlling an explosive underground sealed atmosphere.
An Incremental-Variable-Based State Enumeration Method for Power System Operational Risk Assessment Considering Safety Margin
