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 ...
CatBoost model. CatBoost is a new open source machine learning library proposed by Russian scholar Yandex in 2017, which is based on Categorical and Boosting (Prokhorenkova et al., 2018), a new gradient boosting algorithm that is implemented as a symmetric decision treebased ordered boosting, it improves the gradient estimation of the traditional Gradient Boosting Decision Tree ...
Large foreign object transporting by coal mine conveyor belt may lead to production safety hazards. To reduce safety accidents during coal mining, a large foreign object detection method based on machine vision is proposed in this paper. An adaptive weighted multiscale Retinex (MSR) image enhancement algorithm is proposed to improve the captured image quality of the belt conveyor line. An ...
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.
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 ...
Wu et al. [44] proposed an outburst prediction method based on optimized SVM in 2020, and Zhou et al. [45] used the TreeNet algorithm to predict coal and gas outbursts. The prediction of coal and gas outbursts based on machine learning has achieved good results on the data provided by the author, but it still has two shortcomings.
article{osti_, title = {Development of novel dynamic machine learningbased optimization of a coalfired power plant}, author = {Blackburn, Landen D. and Tuttle, Jacob F. and Andersson, Klas and Fry, Andrew and Powell, Kody M.}, abstractNote = {The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coalfired boilers now routinely ramp up and down.
sieving machine sor ts raw coal into coal equal to or greate r than 100 mm and less than 100 mm; a transp ortation syste m is used to transport the coa l from underground to grou nd; and
Coal resources play a crucial role as an energy source in China and have contributed immensely to the country's economic development [1,2], and given China's current energy structure, coal is expected to maintain its dominant position in the energy supply for the foreseeable future [].Based on statistics from the National Bureau of Statistics, China is endowed with abundant coal resources ...
Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model As the core of artificial intelligence, machine learning has strong application advantages in multicriteria intelligent evaluation and decisionmaking. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises.
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 ...
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 ...
Spontaneous 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 other geomining factors. Hence, the prediction of spontaneous combustion susceptibility of coal is ...
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 et al., 2020), and Cr has been used in ...
Therefore, this manuscript proposes a new identification 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 Vegetation ...
Hui Zhao. Earth Science Informatics (2023) To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on ...
However, in the prediction of coal and gas outbursts, it is difficult or impossible to collect some index data when an accident occurs, which makes less data available for algorithm learning. Therefore, the prediction of coal and gas outbursts based on machine learning is still in the theoretical research stage.
The toplevel architecture of 5G+ intelligent coal mine systems combines intelligent applications such as autonomous intelligent mining, humanmachine collaborative rapid tunneling, unmanned auxiliary transportation, closedloop safety control, lean collaborative operation, and intelligent ecology.
Coal burner working as a component of an asphalt plant in Thailand. A coal burner (or pulverized coal burner) is a mechanical device that burns pulverized coal (also known as powdered coal or coal dust since it is as fine as face powder in cosmetic makeup) into a flame in a controlled manner. Coal burners are mainly composed of the pulverized coal machine, the host of combustion machine ...
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 ...
et al. [29] used a machine learning model to develop an acceptable coal ash model based on a variable block width incremental random configuration network and proposed an online adaptive semisupervised learning based proper coal ash model [30]. Machine learning tools have been shown to have the ability to provide datadriven mechanical ...
Chemical analysisbased, imagebased, and machinelearningbased methods are widely used for coal identification. The chemical analysisbased method is reliable and relatively accurate. However, this method requires stringent analysis techniques for elemental content, and it is easily affected by foreign chemical substances.
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.
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 ...
The four elements (man, machine, environment, and management) in the coal mine and their synthesis are calculated and analyzed by using the Matlab tool and the BP neural network program. The predicted value of the personnel intrinsic safety value is (). The intrinsic safety value of the equipment is (, ).
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 ...
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 ...
Research on Multistep Mixed Predictiom Model of Coal Gasifier Furnace Temperature Based on Machine Learning February 2022 Journal of Physics Conference Series 2187(1):012070
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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