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., .
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 ...
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 .
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 .
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 ...
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 .
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 .
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 ...
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 .
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]
WEBNov 20, 2022 · Based on differences in coal rock texture features, Meng and Li put forward a GLCM and BPNNbased coal rock interface identifiion 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 .
WEBSep 1, 2023 · Effects of Nibased composite coatings on failure mechanism and wear resistance of cutting picks on coal shearer machine. ... After completing the field studies in a real scale coal cutting machine and measuring the wear rate of the coated and uncoated picks refer to cutting operation length, the results of these measurements were analyzed .
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 .
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.
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 .
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 .
WEBMar 23, 2022 · The technology of microseismic monitoring, the first step of which is event recognition, provides an effective method for giving early warning of dynamic disasters in coal mines, especially mining water hazards, while signals with a low signaltonoise ratio (SNR) usually cannot be recognized effectively by systematic methods. This paper .
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 .
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, .
WEBSep 1, 2023 · Based on reverse engineering, this paper discusses the process of localization and development of imported coal machine reducers and focuses on the five steps from the reducer design stage.
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 .
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 .
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.
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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