Research on identification method of bituminous coal based on terahertz time-domain spectroscopy
The traditional coal type identification method needs to measure a variety of parameters of coal samples to obtain more accurate results, and the detection process is time-consuming and laborious, and can not realize the rapid identification of coal types. In this paper, a bituminous coal species id...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1503835/full |
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| author | Shuguang Miao Shuguang Miao Xiang Liu Xiang Liu Yue Zhang Yue Zhang SuWen Li SuWen Li Enjie Ding Enjie Ding |
| author_facet | Shuguang Miao Shuguang Miao Xiang Liu Xiang Liu Yue Zhang Yue Zhang SuWen Li SuWen Li Enjie Ding Enjie Ding |
| author_sort | Shuguang Miao |
| collection | DOAJ |
| description | The traditional coal type identification method needs to measure a variety of parameters of coal samples to obtain more accurate results, and the detection process is time-consuming and laborious, and can not realize the rapid identification of coal types. In this paper, a bituminous coal species identification method based on terahertz time-domain spectroscopy combined with machine learning-principal component analysis Principal component analysis (PCA) and cluster analysis (CA) was proposed. The two types of bituminous coal samples were detected by the transmission terahertz time-domain spectroscopy system, and the spectral data of various bituminous coal samples were obtained, and then the absorption coefficient and refractive index of each sample were obtained after mathematical calculations such as fast Fourier transform (FFT). The results show that the PCA-CA classification model based on terahertz absorption coefficient spectrum can accurately identify different bituminous coals with an accuracy of 100%, while the PCA-CA classification model based on refractive index spectra cannot accurately identify different bituminous coals. The results show that the terahertz time-domain spectroscopy combined with machine learning algorithm can accurately identify different kinds of bituminous coal, and the model classification effect based on terahertz absorption coefficient spectrum is better than that of the model based on refractive index spectroscopy, which provides a new idea for coal mining and utilization. |
| format | Article |
| id | doaj-art-9e04c10a70224f86bf73cfe4238e436c |
| institution | DOAJ |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-9e04c10a70224f86bf73cfe4238e436c2025-08-20T03:06:30ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-04-011310.3389/feart.2025.15038351503835Research on identification method of bituminous coal based on terahertz time-domain spectroscopyShuguang Miao0Shuguang Miao1Xiang Liu2Xiang Liu3Yue Zhang4Yue Zhang5SuWen Li6SuWen Li7Enjie Ding8Enjie Ding9School of Physics and Electronic Information, Huaibei Normal University, Huaibei, ChinaAnhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, ChinaSchool of Physics and Electronic Information, Huaibei Normal University, Huaibei, ChinaAnhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, ChinaSchool of Physics and Electronic Information, Huaibei Normal University, Huaibei, ChinaAnhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, ChinaSchool of Physics and Electronic Information, Huaibei Normal University, Huaibei, ChinaAnhui Province Key Laboratory of Intelligent Computing and Applications, Huaibei Normal University, Huaibei, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaIOT Perception Mine Research Center, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaThe traditional coal type identification method needs to measure a variety of parameters of coal samples to obtain more accurate results, and the detection process is time-consuming and laborious, and can not realize the rapid identification of coal types. In this paper, a bituminous coal species identification method based on terahertz time-domain spectroscopy combined with machine learning-principal component analysis Principal component analysis (PCA) and cluster analysis (CA) was proposed. The two types of bituminous coal samples were detected by the transmission terahertz time-domain spectroscopy system, and the spectral data of various bituminous coal samples were obtained, and then the absorption coefficient and refractive index of each sample were obtained after mathematical calculations such as fast Fourier transform (FFT). The results show that the PCA-CA classification model based on terahertz absorption coefficient spectrum can accurately identify different bituminous coals with an accuracy of 100%, while the PCA-CA classification model based on refractive index spectra cannot accurately identify different bituminous coals. The results show that the terahertz time-domain spectroscopy combined with machine learning algorithm can accurately identify different kinds of bituminous coal, and the model classification effect based on terahertz absorption coefficient spectrum is better than that of the model based on refractive index spectroscopy, which provides a new idea for coal mining and utilization.https://www.frontiersin.org/articles/10.3389/feart.2025.1503835/fullbituminous coal identificationterahertz spectroscopymachine Learningprincipal component analysiscluster analysis |
| spellingShingle | Shuguang Miao Shuguang Miao Xiang Liu Xiang Liu Yue Zhang Yue Zhang SuWen Li SuWen Li Enjie Ding Enjie Ding Research on identification method of bituminous coal based on terahertz time-domain spectroscopy Frontiers in Earth Science bituminous coal identification terahertz spectroscopy machine Learning principal component analysis cluster analysis |
| title | Research on identification method of bituminous coal based on terahertz time-domain spectroscopy |
| title_full | Research on identification method of bituminous coal based on terahertz time-domain spectroscopy |
| title_fullStr | Research on identification method of bituminous coal based on terahertz time-domain spectroscopy |
| title_full_unstemmed | Research on identification method of bituminous coal based on terahertz time-domain spectroscopy |
| title_short | Research on identification method of bituminous coal based on terahertz time-domain spectroscopy |
| title_sort | research on identification method of bituminous coal based on terahertz time domain spectroscopy |
| topic | bituminous coal identification terahertz spectroscopy machine Learning principal component analysis cluster analysis |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1503835/full |
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