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 ...
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 ...
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.
Coal is the most abundant fossil fuel on Earth. Its predominant use has always been for producing heat energy. It was the basic energy source that fueled the Industrial Revolution of the 18th and 19th centuries, and the industrial growth of that era in turn supported the largescale exploitation of coal deposits. Since the mid20th century, coal has yielded its place to petroleum and natural ...
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 ...
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 (, ).
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 ...
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 ...
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 ...
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).
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.
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.
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 ...
Monitoring and enforcing the performance of equipment in coalbased thermal power plants play a vital role in operational management. As the coalbased power plant is a nonlinear system involving multiple inputs and multiple outputs, the standard and typical identification methods tend to deviate. This can happen due to factors such as strong coupling, multivariable characteristics, time ...
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
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 ...
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 ...
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 ...
Highperformance and costeffective GPUbased instances for AI, HPC, and graphics workloads To power the development, training, and inference of the largest large language models (LLMs), EC2 P5e instances will feature NVIDIA's latest H200 GPUs, which offer 141 GBs of HBM3e GPU memory, which is times larger and times faster than H100 GPUs.
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.
Professor Shan Pengfei adopted a coalrock identification method based on machine deep learning FasterRCNN, which realized the accurate identification and location of coal seam and rock stratum ...
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 ...
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.
DOI: / Corpus ID: ; Multiinformation online detection of coal quality based on machine vision article{Zhang2020MultiinformationOD, title={Multiinformation online detection of coal quality based on machine vision}, author={Zelin Zhang and Yang Liu and Qingli Hu and Zhiwei Zhang and Lei Wang and Xiang Liu and Xuhui Xia}, journal={Powder Technology}, year ...
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 ...
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.
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 ...
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 ...
The main obstacle for machine and equipment use that allow coil processing is the quantity to be processed. Naturally, when only a few parts need to be made, sheet metal is the best solution. But even in the case of mediumsized batches, the coil technology is still not very successful, as coil replacement and "production changeover" times ...
Honeycomb Coal Briquette Machine. Honeycomb coal briquette machine can compress small granular coal and dust into coal blocks with holes. Its mold can be changed easily to produce cylindrical shapes and square shape briquettes. The coal briquette diameter range is 90250mm with different hole quantities.
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 ...
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.
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