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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WEBDec 1, 2019 · SVM has good generalization performance and can effectively process problems such as complex data, small sample size, and nonlinear. Wang et al. [36], [37] proposed a coal recognition method (CSVM) based on SVM and coal spectral characteristics. Their results prove that the CSVM is stronger than the traditional coal .
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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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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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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.
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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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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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WEBJun 3, 2021 · This paper uses this as a starting point to propose a distributed support vector machine model based on a cloud computing platform. The model is based on the existing popular MapReduce distributed computing framework, and completes the classifiion and prediction work in the coal system in a distributed manner. ... Environmental cost control ...
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WEBDOI: / Corpus ID: ; Experimental analysis of vibratory screener efficiency based on density variation for screening coal and iron ore article{Shanmugam2023ExperimentalAO, title={Experimental analysis of vibratory screener efficiency based on density variation for screening coal and iron ore}, .
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 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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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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WEBJan 30, 2014 · This paper presents a new online coal identifiion system based on support vector machine (SVM) to achieve online coal identifiion under variable combustion conditions.
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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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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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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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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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WEBDec 5, 2022 · Professor Shan Pengfei adopted a coalrock identifiion method based on machine deep learning FasterRCNN, which realized the accurate identifiion and loion of coal seam and rock stratum ...
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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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