WEBSep 1, 2021 · Among them, the sensorbased equipment is a hightech classifiion method with high efficiency, low cost, and no pollution, so it has the potential for mineral preenrichment and presorting in industrial appliions. At present, sensorbased ore sorting technology is mainly divided into two types: ray sensorbased and machine .
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WEBMay 27, 2021 · To detect the coalcarrying rate in gangue, a new method based on threedimensional (3D) image features and gray wolf optimizationsupport vector machine (GWOSVM) was proposed.
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WEBMay 4, 2023 · Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geomining factors. Hence, the .
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WEBJun 1, 2023 · Feng et al. (2015) proved that a support vector machine (SVM) could perform well in terms of accuracy to predict the gross calorific value (GCV) ... In this study, the GBRT model was used to predict the HHV of coal based on the proximate analysis data, and the model adopted optimal parameters selected through crossvalidation. ...
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WEBApr 26, 2023 · The problem of dust pollution in the openpit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the openpit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the openpit coal mine. It is of practical .
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WEBDec 21, 2021 · A coal gangue recognition method based on improved Support Vector Machine is proposed in this paper, and the experimental results show that the accuracy is %. In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, .
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WEBSep 1, 2020 · Wang et al. [12] quickly analyzed the properties of coal based on support vector machine (SVM) classifier, improved PLS and nearinfrared reflectance the experiment, they first used the SVM classifier to construct a classifiion model for 199 coal samples, and then established a coal quality prediction .
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WEBMar 23, 2022 · The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signaltonoise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper .
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WEBJul 26, 2018 · OAPA. Coal exploration based on the MELM model and Landsat 8 satellite images: (a) image taken on July 5th, 2015; (b) image taken on May 4th, 2016; (c) image taken on June 24th, 2017; (d) Google ...
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WEBThe paper analyzed coal mine safety investment influence factors and established coal mine safety investment prediction model based on support vector machine. Finally, the paper adopted survey data of a mine in Huainan to exemplify and compare with traditional BP network, which proved the method feasibility and effectivity.
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WEBJul 4, 2023 · Based on a particle swarm optimization algorithm and two machine learning algorithms, BP neural network and random forest, a prediction model of tar yield from oilrich coal is constructed in this ...
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WEBJul 1, 2022 · Abstract. In this paper, YOLOv4 algorithm based on deep learning is used to detect coal gangue. Firstly, the data set of coal gangue was made, which provides sufficient data for the training and verifiion of the detection algorithm model. Then, the coal gangue data set was used to test the influence of the combined use of optimization ...
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WEBBecause of its complex working environment, most coal mines take belt conveyor as the main transportation equipment. However, in the process of transportation, due to longtime and highintensity operation, the belt is very easy to be damaged by gangue, iron and other foreign matters doped in coal, resulting in unnecessary losses. Foreign objects in the .
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WEBDec 1, 2014 · Xu et al. propose a coalrock interface recognition method during top coal caving based on Melfrequency cepstrum coefficient (MFCC) and neural network with sound sensor fixed on the tail beam of ...
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WEBApr 5, 2022 · In this section, we discuss several typical coal classifiion methods. The use of machine learning methods in combination with spectroscopy to classify coal is based mainly on ELM, random forest (RF) and support vector machine (SVM) [38], [39]. The comparison results are presented in Table 2. The proposed method outperforms these .
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WEBJan 13, 2022 · Since hundreds or thousands of patches can be extracted from each image, the patch database is much larger than the rock and coal image database. The machine learning process is based on the patches. As discussed earlier, the RGB images are stored as threedimensional arrays, and the extraction of patches is accomplished by extracting .
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WEBAug 15, 2023 · Prediction of gross calorific value as a function of proximate parameters for Jharia and Raniganj coal using machine learning based regression methods. Int J Coal Prep Util, 42 (12) (2022), pp., / View in Scopus Google Scholar [38]
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WEBDec 3, 2021 · Based on the above, this scheme designs the mine belt conveyor deviation fault detection system based on machine vision, uses mine camera to collect images, uses OpenCV visual library compiler software for image processing, carries on the clear processing to the coal mine image, effectively reduces the coal dust influence, .
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WEBSep 1, 2018 · Conclusion. In this study, we proposed a coal proximate analysis model based on a combination of visibleinfrared spectroscopy and deep neural networks. We first collected the spectral data of 100 samples of different types and applied the deep learning CNN and ELM algorithms to construct a coal analysis model.
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WEBA coal mine mantrip at Lackawanna Coal Mine in Scranton, Pennsylvania Coal miners exiting a winder cage at a mine near Richlands, ia in 1974 Surface coal mining in Wyoming, A coal mine in Frameries, Belgium. Coal mining is the process of extracting coal from the ground or from a mine. Coal is valued for its energy content and .
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WEBAug 25, 2021 · Gas explosion has always been an important factor restricting coal mine production safety. The appliion of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas .
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WEBAug 25, 2021 · The appliion of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique .
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WEBApr 2, 2019 · The machinelearningbased workflow provides a new technique for seismic structure interpretation in coal mining. Neural network model. Construction of the hyperplane: φ is the mapping function ...
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WEBNov 1, 2021 · In this study, we developed an automatic Ppick quality control model based on machine learning to identify useable/unusable Ppicks. ... Pd, and As in bulk metallurgical or coalbased solid waste greatly surpasses the standard levels. Nevertheless, by mixing such waste within the coal mine backfill materials, the resulting .
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WEBJul 13, 2023 · Clustering, Classifiion, and Quantifiion of Coal Based on Machine Learning Clustering Models. Clustering is a type of unsupervised learning method, which extracts the data features only based on the LIBS spectra instead of egory labels, including principal component analysis (PCA), Kmeans clustering, DBSCAN clustering, .
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WEBFeb 20, 2023 · Computervisionbased separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like .
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WEBApr 1, 2017 · The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes.
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A material supplier from Belgium would like to produce limestone powder for his custome...
India is rich in various mineral resources and it is an important mineral processing market
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