In our previous work, an approach based on image analysis and particle swarm optimizationsupport vector machine was presented (Wang et al. 2021) to detect the coalcarrying rate in gangue ...
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The machine learning models were optimized using hyperparameter tuning, and the most successful model was selected based on its regression and computational cost performance. Sensitivity analysis was conducted to investigate the performance of the coal properties on total desorbed gas content.
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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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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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Coal mines operated without electricity. Electricity began to be adopted in mining and manufacturing in the late 1880s and the 1890s. (Electricity was first introduced into Ohio's bituminous coal mines in 1889.) The introduction of electricity in coal mines greatly facilitated the introduction of laborsaving machinery. 1891.
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Longwall Miner. Twenty percent to 30 percent of mined coal underground is from longwall mining. This is performed by a mechanical cutter that shears coal off from a panel on the seam. The panel being worked on may be up to 800 feet in width and 7,000 feet in length. Mined coal is deposited onto a conveyor that moves the coal to a collection area.
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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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The four elements (man, machine, environment, and management) in the coal mine and their synthesis are calculated and analyzed by using the Matlab tool and the BP neural network program. The predicted value of the personnel intrinsic safety value is (). The intrinsic safety value of the equipment is (, ).
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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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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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Coal is a black or brownishblack sedimentary rock that can be burned for fuel and used to generate is composed mostly of carbon and hydrocarbons, which contain energy that can be released through combustion (burning). Coal is the largest source of energy for generating electricity in the world, and the most abundant fossil fuel in the United States.
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Therefore, this manuscript proposes a new identification method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small subimages, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered Vegetation ...
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1. Introduction. Metal, as a limited natural resource, is an essential material for global economic development (Sykes et al., 2016).For example, Al and Fe have been widely used in building construction and machinery manufacturing (Soo et al., 2019), V is an important metallic material used in the production of ferrous and nonferrous alloys (Gao et al., 2020), and Cr has been used in ...
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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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sieving machine sor ts raw coal into coal equal to or greate r than 100 mm and less than 100 mm; a transp ortation syste m is used to transport the coa l from underground to grou nd; and
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Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model As the core of artificial intelligence, machine learning has strong application advantages in multicriteria intelligent evaluation and decisionmaking. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises.
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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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Coal has been one of the most important sources of primary energy, together with oil and natural gas, for many decades now. Approximately onethird of the world's energy and 40% of electricity is generated from coal, which will remain an important part of the global energy mix in the medium to long term [1,2].During the early extraction of coal resources, the roomandpillar mining method ...
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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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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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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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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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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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A material supplier from Belgium would like to produce limestone powder for his custome...
India is rich in various mineral resources and it is an important mineral processing market
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