Escape route optimization by cellular automata based on the multiple factors during the coal mine disasters

When disaster occurs in coal mines, the complex network structure of roadways and the disaster evolution process usually cause serious casualties. An efficient escape plan becomes an important method to guarantee survivors. The factors affecting the miners’ escape during the disaster were analyzed. An experimental model was established based on the key factors such as the slope, the convex–concave degree, and the speed of the airflow. The equations between the key factors and the escape speed were fitted by a large number of experimental values. A quantitative model of high temperature, CO, CO2, and O2 concentrations, visibility, and other environmental factors related to the dynamic health of the miners was set up. A cellular automata model for the shortest route selection in the complex ventilation network was established. In order to solve this problem, we established a physical model of the ventilation system in the Tang Shan-gou coal mine. The static difficulty degree of the roadways in the complex network was calculated by classification and segmentation. The parameters such as temperature and the components of the fire smoke in the roadways were simulated by the fire dynamics simulator. The cellular automata model was improved, the static difficulty was set as the primary selection factor, and the dynamic healthy degree was set as the secondary factor. From the simulated disaster drills carried out by the miners and the numerical simulation results, we proved that it is reasonable and feasible to use the improved cellular automata model for the personnel escape route optimization.