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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Abstract. The calorific value of coal is important in both the direct use and conversion into other fuel forms of coals. Accurate calorific value predicting is essential in ensuring the economic, efficient, and safe operation of thermal power plants. Least squares support vector machine (LSSVM) is a variation of the classical SVM, which has ...
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Coal power plant cycling 1. Introduction The use of renewable energy sources (RESs) globally is projected to reach up to 30% by the end of 2030 [1]. In 2020, RES accounted for 21% of all the electricity generated in the United States [2]. The RESs, such as wind and solar, are considered as intermittent generating sources due to climatic conditions.
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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.
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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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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 ...
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Get Price Quote. Voltage : 220V Capacity : 3000 Kgs to 3900 Kg per hour Weight : kg Power Consumption : 1 Hp to 30 Automatic Grade : Automatic used in chemicals, lime stone. bricks industries to make the coal briquettes for firing in furnaces and boilers. by this machine coal briquettes can be made in many shapes designs from coal dust powder with binding material. also used to ...
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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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According to Table 1, the response time of belt conveyor deviation correction system based on machine vision is less than s, and the maximum difference between the deviation detected by machine vision and the actual deviation of sensor is only cm. Thus, this system is capable of quick and effective detecting conveyor belt deviation.
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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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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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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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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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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 burst has become a worldwide problem that needs to be solved urgently for the sake of coal mine safety production due to its complicated triggering mechanisms and numerous influencing factors. The risk assessment of coal burst disasters is particularly critical. In this work, 15 factors affecting coal burst occurrence are selected from the perspectives of geodynamic environment and ...
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October 24, 2022 by Dianna. A coalbased power plant converts coal into electricity. The coal is first pulverized into a fine powder and then burned in a boiler to heat water and produce steam. The steam is then used to drive a turbine that generates electricity. In coalfired power plants, coal is burned to generate steam, which is used to ...
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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 ...
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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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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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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 ...
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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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Muscle stem cells (MuSCs) reside in a niche, which generates various signals essential for regeneration of skeletal muscle. In this manuscript, Togninalli, Ho, and Madl developed a dual fluorescence imaging time lapse (DualFLIT) microscopy approach that leverages machine learning to track single cell fate, their analysis revealed that the lipid metabolite, prostaglandin ...
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Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and ...
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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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Coal mine gas accident is one of the most serious threats in the process of safe coal mine mining, making it important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR ...
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1. Introduction Coal burst is a kind of dynamic disaster in coal mining, and its harm is mainly manifested in roadway destruction, causing casualties and inducing secondary disasters [ 1, 2, 3, 4, 5 ]. Figure 1 shows the field damage of coal bursts in Wudong Coal Mine, China [ 6 ].
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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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