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.
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 ...
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 ...
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.
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.
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.
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.
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 ...
article{osti_, title = {Development of novel dynamic machine learningbased optimization of a coalfired power plant}, author = {Blackburn, Landen D. and Tuttle, Jacob F. and Andersson, Klas and Fry, Andrew and Powell, Kody M.}, abstractNote = {The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coalfired boilers now routinely ramp up and down.
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 ...
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.
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.
At present, coal gangue sorting technology based on machine learning is widely used . Liu C et al. established a comprehensive identification model of different ores and a support vector machine model through the texture characteristics of an image and completed the identification of different ores, thereby improving the efficiency of coal and ...
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 ...
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.
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 ...
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 (, ).
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.
This report presents the results of an exploratory machine learningbased analysis of green stormwater infrastructure asset data across five cities in the United States. Within each city, authors evaluated the location of installed green stormwater infrastructure based on the demographic and land use characteristics of the surrounding area.
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 ...
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 ...
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 ...
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 ( P a ) to ...
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 ...
Hui Zhao. Earth Science Informatics (2023) To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on ...
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
DOI: / Corpus ID: ; Rapid detection of coal ash based on machine learning and Xray fluorescence article{Huang2022RapidDO, title={Rapid detection of coal ash based on machine learning and Xray fluorescence}, author={Jinzhan Huang and Zhiqiang Li and Biao Chen and Sen Cui and Zhaolin Lu and Wei Dai and Yuemin Zhao and Chenlong Duan and Liang Dong}, journal ...
The toplevel architecture of 5G+ intelligent coal mine systems combines intelligent applications such as autonomous intelligent mining, humanmachine collaborative rapid tunneling, unmanned auxiliary transportation, closedloop safety control, lean collaborative operation, and intelligent ecology.
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 ...
Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual ...
et al. [29] used a machine learning model to develop an acceptable coal ash model based on a variable block width incremental random configuration network and proposed an online adaptive semisupervised learning based proper coal ash model [30]. Machine learning tools have been shown to have the ability to provide datadriven mechanical ...
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 ...
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.
Abstract. The higher heating value (HHV) of 84 coal samples including hard coals, lignites, and anthracites from Russia, Colombia, South Africa, Turkey, and Ukrania was predicted by multilinear regression (MLR) method based on proximate and ultimate analysis data. The prediction accuracy of the correlation equations was tested by Analysis of variance method. The significance of the predictive ...
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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