Application of terahertz dielectric constant spectroscopy for discrimination of oxidized coal and unoxidized coal by machine learning algorithms

Zhu, Huangqiu; Wang, Haoran; Liu, Jiuli; Wang, Wei; Gao, Rongxiang; Zhang, Yilong

doi:10.1016/j.fuel.2021.120470

Cited by 21 publications

(5 citation statements)

References 46 publications

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“…Despite the absence of characteristic absorption peaks in the THz spectra of three types of HPC samples, samples can be distinguished in frequency-domain spectra [7].…”

Section: Timementioning

confidence: 99%

Study of different types of hydroxypropyl celluloses (HPC) based on terahertz time-domain spectroscopy (THz-TDS)

Yan

Wang

Guo

et al. 2022

Infrared, Millimeter-Wave, and Terahertz Technologies IX

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Differences in hydroxypropyl cellulose (HPC) include the degree of hydroxypropyl substitution of cellulose or the molecular weight of hydroxypropyl cellulose. The differences have an impact not only on its physicochemical properties but also on the drug delivery process. It is sensitive to a wide range of intermolecular vibrations as well as lattice phonons using Terahertz time-domain spectroscopy (THz-TDS). We have obtained the spectral parameters of various kinds of hydroxypropyl celluloses by THz-TDS measurement. The experimental results show that the spectral characteristics of various kinds of hydroxypropyl celluloses are distinguishable. Using THz-TDS, we can quickly and accurately distinguish different hydroxypropyl celluloses and analyze the physicochemical properties of different hydroxypropyl celluloses.

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“…Despite the absence of characteristic absorption peaks in the THz spectra of three types of HPC samples, samples can be distinguished in frequency-domain spectra [7].…”

Section: Timementioning

confidence: 99%

Study of different types of hydroxypropyl celluloses (HPC) based on terahertz time-domain spectroscopy (THz-TDS)

Yan

Wang

Guo

et al. 2022

Infrared, Millimeter-Wave, and Terahertz Technologies IX

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“…Liansheng Li et al put forward a method for identifying coal and gangue based on density difference 23 , and Feng Xing proposed a non-contact microwave detection technology to detect the mixing degree of coal and gangue 24 . Jingjing Deng et al employed terahertz technology to generate images of coal gangue mixture for the purpose of coal gangue detection 25 – 28 . Yuming Huo optimized the parameters of intelligent coal drawing process by establishing a predictive model of the periodic coal drawing time 29 , 30 .…”

Section: Introductionmentioning

confidence: 99%

Precise detection of coal and gangue based on natural γ-ray

Zhang,

Liu,

Zhu

et al. 2024

Sci Rep

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To address the technical limitations of automatic coal and gangue detection technology in fully mechanized top coal caving mining operations, the low radiation level radioactivity measurement method is utilized to assess the degree of coal-gangue mixture in top coal caving process. This approach is based on the distinguishing radiation characteristics of natural γ-rays between coal and gangue. This study analyzed the distribution characteristics of natural γ-rays in coal and rock layers of thick coal seams and the applicability of this method, introduced the basic principle of coal-gangue detection technology based on natural γ-ray, developed the test system about automatic coal-gangue detection, studied the radiation characteristics of coal and gangue, proposed determination model of the coal-gangue mixed degree, combined with the time sequence characteristics of the top coal’s releasing flow and the energy spectrum characteristics of different layers of rock, realized the precise coal-gangue detection technology in complex structure thick coal seam with multiple gangue. Field tests were conducted in Lilou, Xiaoyu and Tashan Coal Mine. The test results were well corroborated with the research results and achieved the expected results, which laid the foundation for the field application of intelligent coal mining.

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“…They employed Savitzky-Golay (SG) smoothing and orthogonal signal correction (OSC) to pretreat spectra, and applied PCA, partial least squares (PLS) discriminant analysis and SVM to establish classification models for distinguishing sand samples from various deserts and grain sizes. Zhu and Wang et al [12] used logistic regression (LR), SVM, and RF to classify the oxidation degree of coal, and provided a coal spontaneous combustion monitoring technology combining THz permittivity spectrum and machine learning algorithm. Zhang and Li et al [13] used PCA to reduce the dimensionality of original THz spectral information, and then employed SVM, decision tree (DT), and RF to discriminate herbal medicines.…”

Section: Introductionmentioning

confidence: 99%

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Zheng,

Zha,

Cai

et al. 2024

Heliyon

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Application of terahertz dielectric constant spectroscopy for discrimination of oxidized coal and unoxidized coal by machine learning algorithms

Cited by 21 publications

References 46 publications

Study of different types of hydroxypropyl celluloses (HPC) based on terahertz time-domain spectroscopy (THz-TDS)

Study of different types of hydroxypropyl celluloses (HPC) based on terahertz time-domain spectroscopy (THz-TDS)

Precise detection of coal and gangue based on natural γ-ray

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

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