Citation

Zou Q, Liu H, Zhang Y, Li Q, Fu J, Hu Q. Safety Sci. 2020; 122: e104515.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.104515

PMID

unavailable

Abstract

The safe and efficient operation of coal mines is inseparable from rational production deployment. As for outburst-prone coal mines, the production deployment is more complicated due to the introduction of gas disaster control processes, which can easily cause continuity tension and even coal and gas outburst accidents. Therefore, it is necessary to establish an evaluation system for the rationality of production deployment of outburst-prone coal mines. In this paper, based on the production experience of coal mines in Chongqing, China and the summaries of experts, a production deployment evaluation system with eleven indices for outburst-prone coal mines is established. In addition, a Bayesian network is used to establish a corresponding evaluation model. The result shows that the evaluation index system for the production deployment rationality of outburst-prone coal mines, which is composed of the excavation advance indices and the validity indices of regional measure engineering, can systematically diagnose the rationality of each link of the production deployment for outburst-prone coal mines. The results of the case study show that the established Bayesian network model can be used to evaluate the deployment rationality of outburst-prone coal mines. The sensitivity analysis shows that the development coal reserve is the most sensitive to the reasonable deployment of outburst-prone coal mines. The accident prediction and cause diagnosis through the posterior probability reasoning indicate that in the absence of other evidence, the most likely reason for the unreasonable mine deployment is the insufficient development coal reserve. The observations and findings in this research have considerable practical significance for the smooth production of outburst-prone coal mines with similar geological conditions.

Language: en

Keywords

Bayesian network; Excavation advance; Production deployment; Regional measures; Sensitivity analysis