A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs
The core problem in unmanned/intelligent working face of coal mining is the automatic adjustment of shearer arm where the coal-rock interface detection is the key. The cutting location of shearer drum affect the proportion of coal and rock powder around the cutting teeth of shearer drum. Therefore,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9343874/ |
| _version_ | 1819175708138668032 |
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| author | Jing Yu Xin Wang Enjie Ding Jiangbo Jing |
| author_facet | Jing Yu Xin Wang Enjie Ding Jiangbo Jing |
| author_sort | Jing Yu |
| collection | DOAJ |
| description | The core problem in unmanned/intelligent working face of coal mining is the automatic adjustment of shearer arm where the coal-rock interface detection is the key. The cutting location of shearer drum affect the proportion of coal and rock powder around the cutting teeth of shearer drum. Therefore, the method of on-line coal rock interface characterization using Terahertz Time Domain spectroscopy (THz-TDs) we proposed aims to estimate the ratio of rock by Terahertz response. Firstly, anthracite and quartz sandstone were uniformly mixed according to 39 different ratios in this study, the samples' responses were obtained by terahertz system, and then the obtained time domain data was converted into frequency domain data by fast Fourier transform. The absorption coefficient spectrum and the refractive index profile of the 39 samples were calculated by optical parametric model. Secondly, corresponding quantitative model between mixed coal/rock powder and THz signal was built by using back propagation neural network (BPNN) and least squares support vector machine (LSSVM). We expected to use the ratio of rock powder detected by the model to estimate the depth of shearer drum teeth embedded in the rock layer. Finally, we found that both two mathematical arithmetic is feasible to quantitatively detect different proportion of coal and rock mixtures. The results show that the depth of shearer drum teeth embedded in the rock layer could be estimated by the novel method, which means the coal-rock interface could be on-line characterized by using THz-TDs and the height of the drum could be adjusted in time. |
| first_indexed | 2024-12-22T20:59:09Z |
| format | Article |
| id | doaj.art-ef62647385694c38869b1df94f08c1de |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:59:09Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ef62647385694c38869b1df94f08c1de2022-12-21T18:12:52ZengIEEEIEEE Access2169-35362021-01-019258982591010.1109/ACCESS.2021.30561109343874A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDsJing Yu0https://orcid.org/0000-0003-3699-4783Xin Wang1https://orcid.org/0000-0003-2001-8139Enjie Ding2https://orcid.org/0000-0002-1273-076XJiangbo Jing3IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, ChinaIoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, ChinaIoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, ChinaIoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, ChinaThe core problem in unmanned/intelligent working face of coal mining is the automatic adjustment of shearer arm where the coal-rock interface detection is the key. The cutting location of shearer drum affect the proportion of coal and rock powder around the cutting teeth of shearer drum. Therefore, the method of on-line coal rock interface characterization using Terahertz Time Domain spectroscopy (THz-TDs) we proposed aims to estimate the ratio of rock by Terahertz response. Firstly, anthracite and quartz sandstone were uniformly mixed according to 39 different ratios in this study, the samples' responses were obtained by terahertz system, and then the obtained time domain data was converted into frequency domain data by fast Fourier transform. The absorption coefficient spectrum and the refractive index profile of the 39 samples were calculated by optical parametric model. Secondly, corresponding quantitative model between mixed coal/rock powder and THz signal was built by using back propagation neural network (BPNN) and least squares support vector machine (LSSVM). We expected to use the ratio of rock powder detected by the model to estimate the depth of shearer drum teeth embedded in the rock layer. Finally, we found that both two mathematical arithmetic is feasible to quantitatively detect different proportion of coal and rock mixtures. The results show that the depth of shearer drum teeth embedded in the rock layer could be estimated by the novel method, which means the coal-rock interface could be on-line characterized by using THz-TDs and the height of the drum could be adjusted in time.https://ieeexplore.ieee.org/document/9343874/Terahertzquantitative detectionleast squares support vector machineback propagation neural network |
| spellingShingle | Jing Yu Xin Wang Enjie Ding Jiangbo Jing A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs IEEE Access Terahertz quantitative detection least squares support vector machine back propagation neural network |
| title | A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs |
| title_full | A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs |
| title_fullStr | A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs |
| title_full_unstemmed | A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs |
| title_short | A Novel Method of On-Line Coal-Rock Interface Characterization Using THz-TDs |
| title_sort | novel method of on line coal rock interface characterization using thz tds |
| topic | Terahertz quantitative detection least squares support vector machine back propagation neural network |
| url | https://ieeexplore.ieee.org/document/9343874/ |
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