Free Radical Characteristics and Classification of Coals and Rocks Using Electron Spin Resonance Spectroscopy

Miao, Song; Liu, Xiaowen

doi:10.1007/s10812-019-00824-2

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Subsequently, various methods, such as ultrasonic [23] and electromagnetic wave detection [24], can be used to determine the thickness of the top coal. Depending on the different frequency ranges of the electromagnetic waves, ground-penetrating radar [25][26][27], terahertz signals [28,29], and electron resonance identification [30] are additional modalities of detection. In the context of coal caving, numerous scholars have introduced and experimented with various techniques involving radiation [31], visuals [32,33], vibrations [34,35], sounds [36], and infrared spectroscopy recognition [37][38][39] to accurately identify the realtime caving status of coal and gangue.…”

Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

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China has made notable advancements in the intelligent construction of coal mines. However, for longwall top coal caving (LTCC) mining faces, a key obstacle impeding the intelligent transition of the coal-cutting process is automated control. This paper focuses on the aforementioned issue and comprehensively considers the pre-, intra-, and post-coal-caving stages. In this work, diverse detection and monitoring technologies are integrated at various stages through a computer platform, facilitating the construction of an automated coal caving control system with self-perception, self-learning, self-decision-making, and self-execution capabilities. Key technologies include ground-penetrating radar-based top coal thickness detection, inertial navigation-based shearer positioning, tail beam vibration-based identification of coal and gangue, and magnetostrictive sensor-based monitoring of the tail beam and insert plate attitude. In this study, the 12309 working face of the Wangjialing Coal Mine was experimentally validated, and the efficacy of the aforementioned key technologies was assessed. The results demonstrated that the control requirements for automated coal caving are satisfied by the maximum errors. Automatic regulation of coal caving was realized through the implementation of this system, thereby facilitating initiation and cessation and yielding promising experimental outcomes. Overall, this system offers practical insights for intelligent construction in current LTCC mining faces and the sustainable development of coal resources.

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Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

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“…Coal and rock identification technology is a prerequisite for the realization of shearer automation, which is vital to ensure the safety of personnel and to improve the efficiency of coal mining [1]. Traditional coal and rock identification technologies mainly include γ-ray, image recognition, infrared, and radar detection [2][3][4][5]. These methods are affected by harsh mining environments and immature detection technology.…”

Section: Introductionmentioning

confidence: 99%

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

2021

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When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

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“…Therefore, accurate coal hardness detection can assure high coal mining efficiency [3,4]. In recent years, recognition methods have mostly focused on coal-rock image analysis [5,6], multi-sensor information fusion [7,8], acoustic signal analysis [9,10], etc. The idea of the image recognition of coal hardness and type is to compare image features of the working face with those in the database [11].…”

Section: Introductionmentioning

confidence: 99%

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

Liu

et al. 2021

Entropy

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This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

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Free Radical Characteristics and Classification of Coals and Rocks Using Electron Spin Resonance Spectroscopy

Cited by 6 publications

References 14 publications

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

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