WEBMay 1, 2013 · A neural network prediction method based on an improved SMOTE algorithm expanding a small sample dataset and optimizing a deep confidence network was proposed, which can be used to better predict and analyze coal mine water inrush accidents, improve the accuracy of water in rush accident prediction, and encourage the .
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WEBMar 10, 2017 · Gross calorific value (GCV) is one the most important coal combustion parameters for power plants. Modeling of GCV based on coal properties could be a key for estimating the amount of coal consumption in the combustion system of various plants. In this study, support vector regression (SVR) as a powerful prediction method has been .
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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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WEBDOI: / Corpus ID: ; Coal structure identifiion based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSOSVM) algorithms
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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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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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WEBA coal mine mantrip at Lackawanna Coal Mine in Scranton, ... Technical and economic feasibility are evaluated based on the following: regional geological conditions; overburden characteristics; ... It is a sophistied machine with a rotating drum that moves mechanically back and forth across a wide coal seam. The loosened coal falls onto an ...
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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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WEBDOI: / Corpus ID: ; The appliion of machine learning models based on particles characteristics during coal slime flotation article{Zhao2021TheAO, title={The appliion of machine learning models based on particles characteristics during coal slime flotation}, author={Binglong Zhao and .
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WEBDOI: / Corpus ID: ; Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved Unet article{Lei2021MaceralGA, title={Maceral groups analysis of coal based on semantic segmentation of photomicrographs via the improved Unet}, author={Meng Lei and Rao .
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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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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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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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WEBJul 4, 2023 · Based on a particle swarm optimization algorithm and two machine learning algorithms, BP neural network and random forest, a prediction model of tar yield from oilrich coal is constructed in this ...
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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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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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WEBMar 1, 2024 · The above literature is based on gas analysis methods and deploys machine learning to predict coal spontaneous combustion temperature, achieving basically the goal of predicting coal temperature. However, detailed analysis of gas reactions in various stages of coal heating is limited through the literature, resulting in insufficient information ...
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WEBSep 1, 2018 · A coal proximate analysis method based on a combination of visibleinfrared spectroscopy and deep neural networks. This method can fate examines the moisture, ash, volatile matter, fixed carbon, sulphur and low heating value in coal. Compared with traditional coal analysis, this method has unparalleled advantages and .
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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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WEBAug 1, 2021 · IoTenabled sensor devices and machine learning methods have played an essential role in monitoring and forecasting mine hazards. In this paper, a prediction model has been proposed for improving the safety and productivity of underground coal mines using a hybrid CNNLSTM model and IoTenabled sensors. The hybrid CNNLSTM .
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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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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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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 .
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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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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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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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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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