Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction

Abstract

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

“…It updates our probability estimates of events by considering both the prior probability and the observed data, providing more accurate and reliable risk assessment results. The expression of the posterior probability can be seen in Equation (9).…”

“…To deal with the increasingly complex dynamic and operational risk and safety for the coal mining process industry [6], previous research has tried to identify the chance in exploring the solely state-of-the-art methods and models from the view of risk assessment and management for CMTS, such as failure mode and effects analysis (FMEA) [7], risk matrix (RM) [8], statistical process control (SPC) [9], hierarchical analysis [10], and grey relational analysis (GRA) [11]. Specifically, Bayesian network (BN), with the ability of bidirectional inference and efficient analysis of the impact of complex influencing factors on accidents, has been widely applied in the field like hydrogen-doped pipelines [12], offshore drilling operations [13], and so on.…”