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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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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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 ...
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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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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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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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WEBMine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex nonlinear relationship. The paper built the work face gas emission prediction support vector .
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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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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 .
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WEBDOI: / Corpus ID: ; Coal structure identifiion based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSOSVM) algorithms
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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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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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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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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.
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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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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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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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WEBKeeping in mind the various problems related to gas leakage causing accidents in the coal mine, this paper depicts coal monitoring system using wireless sensor networks and IoT, which can monitor the various gas and temperature parameters and take action with the help of multimodal logistic regression algorithm applied on the real time collected data .
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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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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. Based on reverse engineering, this paper discusses the process of localization and .
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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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WEBSep 1, 2021 · The workflow combines physicsbased simulation, laboratory experiments, and a datadriven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses.
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WEBJan 1, 2013 · Maixi Lu, Zhou C (2009) Coal calorific value prediction with linear regression and artificial neural network. Coal Sci Technol 37:117–120. Google Scholar Jiang W, Hongqi W, Qu T (2011) Prediction of the calorific value for coal based on the SVM with parameters optimized by genetic algorithm. Thermal Power Gener 40:14–19
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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 .
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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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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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