Abstract
Coal–rock interface recognition is one of the key unaddressed problems in unmanned mining, so a novel method for it is proposed. Firstly, electron spin resonance (ESR) is used to directly measure 10 kinds of coals/rocks common in China. Secondly, the free radical characteristics of different particle coals/rocks such as the Lande factor g, line width ΔH, and the concentration of the free radical Ng in the X-band ESR are studied. Lastly, the statistical classifier method of support-vector machine is employed to build a classification model with the input of the parameters of the ESR absorption spectra. Based on the ESR-SVM model, the recognition rate of coals/rocks reaches 100%, the recognition rate of different coals reaches 100%, and the recognition rate of different bituminous coals reaches 88.3%. The experimental results demonstrate that the proposed method is fast, stable, and accurate for the detection of the coal–rock interface and can be a promising tool for the classification of different coals.
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