Mine reclamation based on remote sensing information and error compensation extreme learning machine

Xiao, Dong; Xie, Hongfei; Fu, Yanhua; Li, Feifei

doi:10.1080/00387010.2021.1877727

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…Spectral analysis technology, widely employed and developed across multiple fields, offers a noninvasive means to analyze coal properties. − Investigating the interactions between coal and various light wavelengths allows us to deduce its physical and chemical properties, enabling a more precise classification. Additionally, spectral information can facilitate the regression analysis of coal composition.…”

Section: Introductionmentioning

confidence: 99%

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

Li,

Xiao,

Xie

et al. 2023

ACS Omega

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As a principal energy globally, coal’s quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

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Section: Introductionmentioning

confidence: 99%

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

Li,

Xiao,

Xie

et al. 2023

ACS Omega

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“…Li et al [14] accurately monitored rare earth mining in rare earth mining areas based on high-resolution remote sensing images. Xie et al [15] used reflectance spectroscopy and Landsat-8 data to detect the grade of copper mines. Xiao et al [16] used remote sensing data to achieve the inversion of heavy metals in mining areas, which provided guidance for reclamation.…”

Section: Introductionmentioning

confidence: 99%

“…Xie et al [15] used reflectance spectroscopy and Landsat-8 data to detect the grade of copper mines. Xiao et al [16] used remote sensing data to achieve the inversion of heavy metals in mining areas, which provided guidance for reclamation. Le et al [17] used remote sensing data to obtain images of coal distribution in mining areas for coal classification.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

Xu,

Mao,

Zhang

et al. 2023

Remote Sensing

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The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.

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“…The timely measurement information it provides can provide the basis for the identification of coal before combustion and pyrolysis, which can significantly improve efficiency and reduce the consumption of time and resources. In recent years, spectral analysis techniques have been extensively applied in the fields of soil, minerals, food, and so on. − For coal, spectroscopy is used to analyze and identify the category, composition, structure, and other characteristics of coal. Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Xiao

Yan

et al. 2022

ACS Omega

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Coal plays an indispensable role in the world’s energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.

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Mine reclamation based on remote sensing information and error compensation extreme learning machine

Cited by 6 publications

References 26 publications

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

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