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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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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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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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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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 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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WEBDec 1, 2014 · The experimental result shows that feature vectors constructed by the dimensionless parameters input support vector machine can automatically identify the CoalRock interface. In order to identify the CoalRock interface of mechanized caving mining, a new method based on dimensionless parameters and support vector machine .
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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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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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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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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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WEBDec 15, 2022 · Two machine learning techniques, the naive Bayes classifier and support vector machines (SVMs), were employed to achieve the objective. The algorithm was developed based on the dependency of the indiing gas amount on the coal temperature. The accuracy of the techniques was assessed using the nonconformity matrix and .
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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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WEBApr 1, 2023 · In this study, we used machine learning based approach to classify fuels with the use of proximate analysis results,, fixed carbon, volatile matter and ash contents.
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WEBSep 1, 2018 · A coal proximate analysis method based on a combination of visibleinfrared spectroscopy and deep neural networks. This method can fate examines the moisture, ash, volatile matter, fixed carbon, sulphur and low heating value in coal. Compared with traditional coal analysis, this method has unparalleled advantages 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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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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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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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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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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WEBMar 15, 2024 · The life cycle inventory of coal power generation in China was obtained from the CPLCID® (Chinese processbased life cycle inventory database, Zhang et al., 2016), which primarily includes an internationally peerreviewed inventory of subcritical, supercritical, and ultrasupercritical technologies for coal power generation (Hong et al., .
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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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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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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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WEBThis paper presents a novel coal mill modeling technique using genetic algorithms (GA) based on routine operation data measured onsite at a National Power (NP) power station, in England, The work focuses on the modeling of an Etype vertical spindle coal mill. The model performances for two different mills are evaluated, covering a whole range of .
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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 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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WEBSep 7, 2023 · [Show full abstract] the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal ...
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WEBBituminous coal is the most abundant rank of coal found in the United States, and it accounted for about 46% of total coal production in 2022. Bituminous coal is used to generate electricity and is an important fuel and raw material for making coking coal for the iron and steel industry. Bituminous coal was produced in at least 16 states ...
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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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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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