粉尘环境下典型煤岩近红外光谱特征及识别方法

doi:10.3964/j.issn.1000-0593(2020)11-3430-08

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Abstract In order to study the dust problem existing in the identification of near-infrared coal and rock in the underground coal mine, the mixture of anthracite and the anti-explosive agent was used to simulate the dust environment of coal mine underground so that the experimental device for spectral identification of coal and rock in dust environment was constructed. 23 samples of in-situ typical coal samples of shale, sandstone and limestone samples and anthracite, bituminous coal and lignite coal samples were collected from all over the country. 23 coal and stone samples reflection spectrum collecting without the dust of the near-infrared band (1 000～2 500 nm) was used as an experimental standard database. 1 sample was randomly selected from the three typical coal samples and three typical rock samples in the experimental standard the sample library as experimental samples, and the test samples’ reflectance spectra of the near-infrared bands at 600, 1 000, 1 500 and 3 000 mg·m^-3 dust concentrations were collected. The results showed that the addition of dust led to a decrease in the signal-to-noise ratio of the spectral image between 1 000~1 200 and 2 400~2 500 nm; With the increase of dust concentration, The opaque substance of anthracite in the dust made the characteristic absorption valley in the experimental sample weaken; Correlation analysis between sample and standard sample library was carried out by spectral angle matching SAM and Pearson correlation coefficient. Anthracite samples, bituminous coal samples, sandstone samples and limestone samples had a high matching degree under SAM matching model. The cosine angle was above 0.9 at each dust concentration; Correlation coefficient matching model matching degree was strongly affected by dust, and the average correlation coefficient was 0.73; After the experimental standard database and special envoy samples were normal preprocessed by SG convolution and SNV standard, the matching degree of SAM matching model did not change significantly. Correlation coefficient matching model matching degree was significantly improved, the average correlation coefficient was 0. 78; The correlation coefficient matching model excepted for lignite No.2, the spectral correlation coefficient of all samples increased by 0.13, anthracite No.2 The sample correlation coefficient increased by 76.3%, while the spectral correlation coefficient of sample 12 lignite No.2 was reduced by spectral pretreatment. The spectral angle matching SAM and Pearson correlation coefficient coal and rock identification model was established. The two models were used to identify coal samples under different concentrations and binarized coal rock sample, the coals were “0”, and the rocks were “1”. Coal rock identification was performed on 6 experimental samples at different concentrations by two recognition models. The identification accuracy of SAM was 100%, and the recognition time was 8 ms. Pearson correlation recognition accuracy of the coefficient P was 87.5%, and the recognition time was 852 ms.

Key words：Coal and rock identification; Typical coal rock; Near infrared spectroscopy; Dust environment

Received: 2019-11-11 Accepted: 2020-03-20

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TD67

Corresponding Authors: WANG Shi-bo E-mail: wangshb@cumt.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)11-3430-08 OR https://www.gpxygpfx.com/EN/Y2020/V40/I11/3430