Near-infrared reflectance spectrum data preprocessing method for coal gangue identification
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摘要: 利用近红外反射光谱进行煤矸识别时，光谱采集装置距工作面的探测距离变化及粉尘干扰会对近红外反射光谱产生影响。为选取最佳的煤矸近红外反射光谱预处理方法，收集了外观相近的无烟煤和矸石样本，在实验室搭建了由近红外光谱仪、准直镜、卤素灯等组成的光谱采集装置，采集了不同探测距离（1.2，1.5，1.8 m）和粉尘浓度（200，500，800 mg/m3）下的煤矸近红外反射光谱。通过对煤矸近红外反射光谱特征分析，发现探测距离和粉尘浓度变化对煤矸近红外反射光谱曲线波形和吸收谷位置无明显影响，即不会改变光谱特征吸收波长点，但对煤矸近红外反射光谱的反射率产生明显影响，即光谱反射率随着探测距离和粉尘浓度的增大而减小，会造成煤矸近红外反射光谱漂移。为增强煤矸近红外反射光谱吸收特征，利用微分、标准正态变量变换和多项式平滑3种方法对光谱数据进行预处理，并将经过预处理的煤矸近红外反射光谱数据输入至粒子群优化BP神经网络模型进行煤矸识别。实验结果表明，微分预处理方法对探测距离和粉尘浓度变化下采集的煤矸近红外反射光谱数据的优化效果最佳，能有效消除探测距离和粉尘浓度变化对光谱反射率的影响。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.
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