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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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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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.
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Accordingly, eigenvectors of coal and rock images are computed based on thermal imaging cloud images from coal and rock cutting trials. The traditional recognition technology of coal and rock mainly adjusts the height of the drum of the coal winning machine by manually observing the state of coal and rock and listening to the sound.
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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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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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Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety.
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Coloradobased TriState Generation and Transmission Association is proposing an energy plan that will close two coal power plants and significantly boost the amount of renewable energy sources on its system.. TriState filed the new electric resource plan with state regulators Friday. The wholesale power supplier is seeking up to 970 million in grants and loans through the Department of ...
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Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coalfired power plants. However, the combustion of coal in the coalfired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be ...
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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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In previous research, many scientists and researchers have carried out related studies about the spontaneous combustion of coal at both the micro and the macro scales. However, the macroscale study of coal clusters and piles cannot reveal the nature of oxidation and combustion, and the mesoscale study of coal molecule and functional groups cannot be directly applied to engineering practice ...
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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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The proposed coalgangue recognition approach based on MBCNN and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.
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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
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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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Accumulators give off a circuit network signal. You can wire them to a power switch to isolate your steam engines as long as demand is being met elsewhere. If the accumulator falls below a threshold, toggle the engines back on. Look up how to make an SR latch (aka a memory toggle) with combinators.
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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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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 ...
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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 ...
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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 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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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 ...
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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.
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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 classification model for 199 coal samples, and then established a coal quality prediction model for each coal type ...
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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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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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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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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 model can extract ...
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الهند غنية بالموارد المعدنية المختلفة وهي سوق مهم لمعالجة المعادن
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