Near-infrared reflectance spectrum data preprocessing method for coal gangue identification

Abstract

When using near-infrared reflectance spectrum to identify coal gangue, the change of detection distance between spectrum acquisition device and working face and dust interference will affect near-infrared reflectance spectrum. In order to select the best pre-processing method for near infrared reflectance spectrum for coal gangue, samples of anthracite and gangue with similar appearance are collected. A spectrum acquisition device consisting of near infrared spectrometer, collimator and halogen lamp is set up in the laboratory to acquire near infrared reflectance spectrum of coal gangue at different detection distances (1.2,1.5,1.8 m) and dust concentrations (200, 500, 800 mg/m3). Through the analysis of near-infrared reflectance spectrum characteristics of coal gangue, it is found that the detection distance and dust concentration change have no obvious impact on the waveform of near-infrared reflectance spectrum curve and the position of absorption valley of coal gangue. The absorption wavelength point of spectral characteristics will not be changed. However, the reflectance of near-infrared reflectance spectrum of coal gangue will be significantly affected. The spectral reflectance will decrease with the increase of detection distance and dust concentration, which will cause near-infrared reflectance spectrum drift of coal gangue. In order to enhance the absorption characteristics of near-infrared reflectance spectrum of coal gangue, the spectrum data are preprocessed by differential, standard normal variable transformation and polynomial smoothing methods. The preprocessed near-infrared reflectance spectrum data of coal gangue are input to the particle swarm optimization BP neural network model for coal gangue identification. The experimental results show that the differential preprocessing method has the best optimization effect on the near-infrared reflectance spectrum data of coal gangue collected under the change of detection distance and dust concentration, and can eliminate the impact of detection distance and dust concentration on the spectral reflectance effectively.

Keywords

• coal gangue identification; near-infrared reflectance spectrum; data preprocessing; detection distance; dust concentration