Identification Method of Coal and Coal Gangue Based on Dielectric Characteristics

Abstract

To solve the problems of the difficult feature extraction, poor feature credibility and low recognition accuracy of coal and gangue, this paper utilizes the difference in the dielectric properties of coal and gangue and in combination with a support vector machine (SVM) to propose a recognition method based on the dielectric characteristics of coal and gangue. The influence rule of the edge effect of the electrode plate on the capacitance value is analyzed when the thickness of the electrode plate changes. By changing the frequency and voltage of the excitation source, curves of the dielectric constant of coal and gangue versus frequency and voltage are obtained. Combined with the Kalman filter, the adaptive noise complete set empirical mode decomposition (CEEMDAN) denoising method is improved, which results in a signal with a higher signal-to-noise ratio and lower root mean square error after denoising. The effective value and frequency of the denoised response signal are extracted to construct the feature vector set to form the training set and test set. The data of the training set are input into the SVM to train the intelligent classification model, the test set is used to test the SVM classification effect, and the classification accuracy is 100%. Unlike these of the probabilistic neural network (PNN) intelligent classification model and the learning vector quantization (LVQ) neural network classification model, the recognition and classification accuracies of the three can reach 100%, but the classification speed of SVM is the fastest, only taking 0.007916 s, which fully reflects the feasibility and efficiency of the capacitance method in identifying coal gangue. In this paper, the capacitance method and SVM are applied to identify coal and gangue, and accurate and efficient identification results are obtained, providing a new feasible solution for research on coal gangue identification.