Study on the Prediction Method of Fusion Recognition for Characteristic Information of Coal Spontaneous Combustion

Abstract

Abstract-To solve the technical problems of identification for the dangerous degree of coal spontaneous combustion, the coal spontaneous combustion process is divided into three stages: slow oxidation, accelerating oxidation and intense oxidation in the paper. The prediction method of fusion identification for characteristic information of coal spontaneous combustion is proposed and the sensitivity index of coal spontaneous combustion degree is determined. The quantitative relationship of the characteristic temperature of coal spontaneous combustion and the gas concentration of each index is determined by polynomial least squares fitting method according to the sample test data. The different feature states are classified by SVM and PSO-SVM algorithm. The criterion of the diagnosis and early warning of coal spontaneous combustion is given according to the results of the data level and feature level. The proposed method can effectively solve the problem of low recognition rate. The experiment shows that the prediction classification accuracy of SVM is 80%, the prediction classification accuracy of PSO -SVM is approximately 100%. The PSO-SVM algorithm can significantly improve the prediction accuracy compared with the traditional method, which provides criterion for the diagnosis and early warning of coal spontaneous combustion. The classification identification of the dangerous degree of coal spontaneous combustion is implemented. It is of great significance and practical application value for improving the level of prevention and control technology of coal spontaneous combustion early hazards.