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 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,, .
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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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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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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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WEBDec 8, 2023 · Liu et al. realized the approximate analysis of coal based on laserinduced breakdown spectra by combining principal component regression, artificial neural network, and PCAANN models. All of the above methods are used to deal with highdimensional spectral data using machine learning, but the direct use of machine learning algorithms .
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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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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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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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WEBDec 1, 2014 · Xu et al. propose a coalrock interface recognition method during top coal caving based on Melfrequency cepstrum coefficient (MFCC) and neural network with sound sensor fixed on the tail beam of ...
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WEBJun 1, 2019 · Wang et al. [13] constructed a classifiion model of coal based on a confidence machine, a support vector machine algorithm and nearinfrared spectroscopy, and a good classifiion result was obtained.
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WEBAug 15, 2023 · Prediction of gross calorific value as a function of proximate parameters for Jharia and Raniganj coal using machine learning based regression methods. Int J Coal Prep Util, 42 (12) (2022), pp., / View in Scopus Google Scholar [38]
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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 .
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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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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 .
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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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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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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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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, .
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WEBDec 15, 2021 · The subclass level classifiion also obtained good results with an accuracy of and F1 score of The results demonstrate the effectiveness of rapid coal classifiion systems based on DRS dataset in combination with different machine learningbased classifiion algorithms.
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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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WEBMay 4, 2023 · 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 .
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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 ...
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WEBSep 1, 2021 · Among them, the sensorbased equipment is a hightech classifiion method with high efficiency, low cost, and no pollution, so it has the potential for mineral preenrichment and presorting in industrial appliions. At present, sensorbased ore sorting technology is mainly divided into two types: ray sensorbased and machine .
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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 5, 2022 · In this section, we discuss several typical coal classifiion methods. The use of machine learning methods in combination with spectroscopy to classify coal is based mainly on ELM, random forest (RF) and support vector machine (SVM) [38], [39]. The comparison results are presented in Table 2. The proposed method outperforms these .
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WEBOct 1, 2021 · By combining cablebased parallel robotics and machine vision, it is proposed to detect rusted bolts and leaks at the liner edges during coal bunker maintenance [18]. With lowcost equipment 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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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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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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WEBNov 1, 2021 · 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 .
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