WEBJun 1, 2019 · Wang et al. [9], [10] proposed a coal component analysis model based on a support vector machine, a partial least squares regression algorithm and nearinfrared reflectance spectroscopy. The model analyzed six components of coal, including total moisture, inherent moisture, ash, volatile matter, fixed carbon, and sulfur.
WEBNov 20, 2022 · Based on differences in coal rock texture features, Meng and Li put forward a GLCM and BPNNbased coal rock interface identifiion method. Wu and Tian ; Wu, Zhang proposed a ... Deep learning is a machine learning method based on a deep network model. To be specific, inspired by the concept of "receptive field" in the .
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. ...
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.
WEBApr 1, 2023 · Fig. 1 compares the surface state differences of coal and gangue in various situations based on the proposed model. In the ideal laboratory environment, the light intensity is high, the coal and gangue image acquisition process is simple, and the camera receives more light signals, so it is easy to distinguish coal and gangue; however, in the .
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, .
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 .
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, .
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 .
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,, .
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 ...
WEBJul 26, 2018 · Third, we proposed a multilayer extreme learning machine algorithm and constructed a coal classifiion model based on that algorithm and the spectral data. The model can assist in the classifiion of bituminous coal, lignite, and noncoal objects.
WEBJun 1, 2022 · Accordingly, eigenvectors of coal and rock images are computed based on thermal imaging cloud images from coal and rock cutting trials. The traditional recognition technology of coal and rock mainly adjusts the height of the drum of the coal winning machine by manually observing the state of coal and rock and listening to the sound.
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 .
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.
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 ...
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 .
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 .
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 .
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 .
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 .
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)
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 ...
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 .
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 .
WEBDOI: / Corpus ID: ; Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved Unet article{Lei2021MaceralGA, title={Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved Unet}, author={Meng Lei and Rao .
WEBNov 1, 2020 · Simultaneous quantitative analysis of nonmetallic elements in coal by laserinduced breakdown spectroscopy assisted with machine learning. Author links open ... According to all data obtained in this work, it is reasonable to deduce conclude that LIBS technology based on and machine learning model could be a practical algorithm for .
WEBJan 1, 2023 · The DNN memorybased models show significant superiority over other stateoftheart machine learning models for short, medium and long range predictions. The transformerbased model with attention enhances the selection of historical data for multihorizon forecasting, and also allows to interpret the significance of internal power plant ...
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 .
WEBAbstract. Read online. The classifiion of surrounding rock stability of coal roadway has important theoretical and practical significance for the design, construction and management of onsite rock mass paper selected seven key indexes that affect the surrounding rock stability of coal roadway, collected the samples through field .
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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