WEBOct 1, 2021 · By combining cablebased parallel robotics and machine vision, it is proposed to detect rusted bolts and leaks at the liner edges during coal bunker maintenance [18]. With lowcost equipment and ...
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WEBDec 15, 2021 · The subclass level classifiion also obtained good results with an accuracy of and F1 score of The results demonstrate the effectiveness of rapid coal classifiion systems based on DRS dataset in combination with different machine learningbased classifiion algorithms.
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WEBOct 22, 2023 · The belt conveyor is a key piece of equipment for thermal power plants. Belt mistracking causes higher economic costs, lower production efficiency, and more safety accidents. The existing belt correction devices suffer from poor performance and high costs. Therefore, a design method for coal conveying belt correction devices is proposed in .
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WEBSep 1, 2023 · With the trend of localization of imported coal machine reducers being imperative, the traditional reducer development method has the problems of a high failure rate in the design stage, a long development cycle, and high manufacturing costs. ... Liu X 2020 Research on coal machine spare parts localization based on 3D measurement .
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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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WEBJan 1, 2024 · However, structural complexity and diversity of coals make it face huge challenge. In this study, a predictive model for morphological sulfur migration was developed using machine learning based on proximate analysis, ultimate analysis, sulfur forms of raw coal, ash composition, and blending ratio of coal. Three algorithms,, .
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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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WEBSep 1, 2023 · Based on reverse engineering, this paper discusses the process of localization and development of imported coal machine reducers and focuses on the five steps from the reducer design stage.
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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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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, 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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WEBDec 23, 2022 · failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. T o accurately classify the CBL level, the supportvectormachine (SVM)
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WEBDec 8, 2023 · Liu et al. realized the approximate analysis of coal based on laserinduced breakdown spectra by combining principal component regression, artificial neural network, and PCAANN models. All of the above methods are used to deal with highdimensional spectral data using machine learning, but the direct use of machine learning algorithms .
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WEBDOI: / Corpus ID: ; The appliion of machine learning models based on particles characteristics during coal slime flotation article{Zhao2021TheAO, title={The appliion of machine learning models based on particles characteristics during coal slime flotation}, author={Binglong Zhao and .
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WEBApr 12, 2022 · Machine learning prediction of calorific value of coal based on the hybrid analysis. April 2022. International Journal of Coal Preparation and Utilization 43 (1):122. DOI: / ...
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WEBAug 1, 2021 · IoTenabled sensor devices and machine learning methods have played an essential role in monitoring and forecasting mine hazards. In this paper, a prediction model has been proposed for improving the safety and productivity of underground coal mines using a hybrid CNNLSTM model and IoTenabled sensors. The hybrid CNNLSTM .
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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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WEBFeb 1, 2024 · Coal structure identifiion based on PSOSVM. In this study, the coal structure prediction model was established based on 175 sets of data (53 undeformed coal, 67 aclastic coal and 54 granulated coal) from 20 wells, excluding 10 sets of data from the No. 3 coal seam in Well M19 (4 undeformed coal, 1 aclastic coal and 2 .
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WEBMay 1, 2013 · A neural network prediction method based on an improved SMOTE algorithm expanding a small sample dataset and optimizing a deep confidence network was proposed, which can be used to better predict and analyze coal mine water inrush accidents, improve the accuracy of water in rush accident prediction, and encourage the .
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WEBDec 13, 2023 · The HTG diagrams are established based on previous work by Liu et al. 72 using coal as the investigated feedstock. HHV higher heating value, ER energy recovery, CR carbon recovery.
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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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WEBJan 4, 2024 · Cocombustion of coal and biomass has the potential to reduce the cost of power generation in plants. However, because of the high content of the alkali metal of biomass ash, cocombustion of these two fuels leads to unpredictable ash fusion temperature (AFT). This study conducted experiments to measure the AFT of straw, .
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WEBMar 1, 2024 · The above literature is based on gas analysis methods and deploys machine learning to predict coal spontaneous combustion temperature, achieving basically the goal of predicting coal temperature. However, detailed analysis of gas reactions in various stages of coal heating is limited through the literature, resulting in insufficient information ...
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WEBTherefore, this manuscript proposes a new identifiion method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small subimages, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered .
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WEBMay 1, 2023 · 1. Introduction. Metal, as a limited natural resource, is an essential material for global economic development (Sykes et al., 2016).For example, Al and Fe have been widely used in building construction and machinery manufacturing (Soo et al., 2019), V is an important metallic material used in the production of ferrous and nonferrous alloys (Gao .
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WEBMar 10, 2017 · Gross calorific value (GCV) is one the most important coal combustion parameters for power plants. Modeling of GCV based on coal properties could be a key for estimating the amount of coal consumption in the combustion system of various plants. In this study, support vector regression (SVR) as a powerful prediction method has been .
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WEBJan 1, 2007 · The support vector machines (SVM) model with multiinput and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM ...
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WEBSpontaneous 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 oth .
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WEBOct 22, 2021 · Appliion of Volume Detection Based on Machine Vision in Coal and Gangue Separation. October 2021. DOI: / Conference: 2021 IEEE 5th Conference on Energy Internet and ...
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WEBJun 1, 2019 · Wang et al. [13] constructed a classifiion model of coal based on a confidence machine, a support vector machine algorithm and nearinfrared spectroscopy, and a good classifiion result was obtained. Gomez et al. [14] used Fourier transform infrared photoacoustic spectroscopy combined with partial least squares to predict ash .
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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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