At present, coal gangue sorting technology based on machine learning is widely used . Liu C et al. established a comprehensive identification model of different ores and a support vector machine model through the texture characteristics of an image and completed the identification of different ores, thereby improving the efficiency of coal and ...
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Subsequently, a multiscale linear filter based on the Hessian matrix and Gaussian function was developed to obtain the edge intensity image. Finally, Experiment. The detection experiment of the coal content in gangue was carried out on the test rig shown in Fig. 10. The experimental samples were collected from the Hongliu coal preparation plant.
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Monitoring and enforcing the performance of equipment in coalbased thermal power plants play a vital role in operational management. As the coalbased power plant is a nonlinear system involving multiple inputs and multiple outputs, the standard and typical identification methods tend to deviate. This can happen due to factors such as strong coupling, multivariable characteristics, time ...
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DOI: / Corpus ID: ; Rapid detection of coal ash based on machine learning and Xray fluorescence article{Huang2022RapidDO, title={Rapid detection of coal ash based on machine learning and Xray fluorescence}, author={Jinzhan Huang and Zhiqiang Li and Biao Chen and Sen Cui and Zhaolin Lu and Wei Dai and Yuemin Zhao and Chenlong Duan and Liang Dong}, journal ...
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Online estimation of ash content in coal based on machine vision has been paid more attention to by academia and industry. Existing research has mainly focused on feature extraction and model design for estimating ash content, but the exploration of the feature's contribution to the model is rarely reported.
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The main obstacle for machine and equipment use that allow coil processing is the quantity to be processed. Naturally, when only a few parts need to be made, sheet metal is the best solution. But even in the case of mediumsized batches, the coil technology is still not very successful, as coil replacement and "production changeover" times ...
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Coal burst has become a worldwide problem that needs to be solved urgently for the sake of coal mine safety production due to its complicated triggering mechanisms and numerous influencing factors. The risk assessment of coal burst disasters is particularly critical. In this work, 15 factors affecting coal burst occurrence are selected from the perspectives of geodynamic environment and ...
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Clustering, Classification, and Quantification 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 category labels, including principal component analysis (PCA), Kmeans clustering, DBSCAN clustering, etc. The ...
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Product quality monitoring is one of the most critical demands in the coal industry. Conventional coal quality analysis is offline, laborious, and lagging behind coal production. Using machine vision for determining ash content in coal has been recently developed. However, there are some challenges in the model design due to its task complexity.
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This paper presents an exploratory study employing a benchscale approach to detect the multiinformation of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content.
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Coal has been one of the most important sources of primary energy, together with oil and natural gas, for many decades now. Approximately onethird of the world's energy and 40% of electricity is generated from coal, which will remain an important part of the global energy mix in the medium to long term [1,2].During the early extraction of coal resources, the roomandpillar mining method ...
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Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual ...
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Highperformance and costeffective GPUbased instances for AI, HPC, and graphics workloads To power the development, training, and inference of the largest large language models (LLMs), EC2 P5e instances will feature NVIDIA's latest H200 GPUs, which offer 141 GBs of HBM3e GPU memory, which is times larger and times faster than H100 GPUs.
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The underground coal mines (UCM) exhibit many lifethreatening hazards for mining workers. In contrast, gas hazards are among the most critical challenges to handle. This study presents a comparative study of the sensor fusion methodologies related to UCM gas hazard prediction and classification. The study provides a brief theoretical background of the existing methodologies and their usage to ...
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1. Introduction Coal burst is a kind of dynamic disaster in coal mining, and its harm is mainly manifested in roadway destruction, causing casualties and inducing secondary disasters [ 1, 2, 3, 4, 5 ]. Figure 1 shows the field damage of coal bursts in Wudong Coal Mine, China [ 6 ].
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The nearinfrared spectroscopy (NIRS) technique provides a rapid and nondestructive method for coal proximate analysis. We exploit two regression methods, random forest (RF) and extreme learning machine (ELM), to model the relationships among spectral data and proximate analysis parameters. In addition, given the poor stability and robustness ...
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Coal Classification Method Based on Improved Local Receptive FieldBased Extreme Learning Machine Algorithm and VisibleInfrared Spectroscopy PMC Journal List ACS Omega (40); 2020 Oct 13 PMC As a library, NLM provides access to scientific literature.
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Coal mines operated without electricity. Electricity began to be adopted in mining and manufacturing in the late 1880s and the 1890s. (Electricity was first introduced into Ohio's bituminous coal mines in 1889.) The introduction of electricity in coal mines greatly facilitated the introduction of laborsaving machinery. 1891.
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A novel approach based on binocular machine vision and genetic algorithmbackpropagation neural network (GABPNN) was proposed. First, the sample image was segmented, and each region was judged to be coal or gangue. ... Prediction of density and sulfur content level of highsulfur coal based on image processing. Powder Technol., 407 (2022), p ...
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Based on differences in coal rock texture features, Meng and Li put forward a GLCM and BPNNbased coal rock interface identification 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 biological community ...
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In this study, we developed an automatic Ppick quality control model based on machine learning to identify useable/unusable Ppicks. We used five waveform parameters, including signaltonoise ratio (SNR), signaltonoise variance ratio (SNVR), Pphase startingup slope ( K p ), shorttime zerocrossing rate (ZCR) and peak amplitude ( P a ) to ...
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India aims to add 17 gigawatts of coalbased power generation capacity in the next 16 months, its fastest pace in recent years, to avert outages due to a record rise in power demand, according to ...
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Accurate prediction of coalbed methane (CBM) content plays an essential role in CBM development. Several machine learning techniques have been widely used in petroleum industries (, CBM content predictions), yielding promising results. This study aims to screen a machine learning algorithm out of several widely applied algorithms to estimate CBM content accurately. Based on a comprehensive ...
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In a coal based thermal power plant, the initial process in the power generation is "Coal Handling'. Boiler used in the power plant is suspended type. Search for: ... It consists of a rotary machine, which rotates the coal and separates the light dust from it through the action of gravity and transfer this dust to reject bin house through belt.
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Here, a modeling method based on feature fusion and long shortterm memory (LSTM) network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy. ... Prediction of SOxNOx emission from a coalfired CFB power plant with machine learning: Plant data learned by deep neural ...
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Gas explosion has always been an important factor restricting coal mine production safety. The application 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 concentration. Considering there exist very few instances of high ...
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The imageanalysis based sensors are the most appropriate detection method at present. One option to detect coal quality via multiinformation online is the machine vision detection based on CCD/CMOS industrial cameras, which provides advantages including safety, convenient installation, and highcost performance.
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Coal liquefaction is a process of converting coal into liquid hydrocarbons: liquid fuels and process is often known as "Coal to X" or "Carbon to X", where X can be many different hydrocarbonbased products. However, the most common process chain is "Coal to Liquid Fuels" (CTL).
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The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and ...
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efficiency. Both coal and gasbased DRI plants are operational in India. However, the share of coalbased DRI production is quite substantial and in comparison to gasbased production, this route is energy and carbonintensive. To meet the DRI production target of 80 million tonne by 203031 as envisaged under the
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يرغب أحد موردي المواد من بلجيكا في إنتاج مسحوق الحجر الجيري لملابسه...
الهند غنية بالموارد المعدنية المختلفة وهي سوق مهم لمعالجة المعادن
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