Analysis of Risk Management for the Coal Mine Operations

Nanjing,; Zhenjiang,; Suqian,

doi:10.4236/epe.2017.94b002

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Coal ash blast intensity characteristics mainly include flame, pressure, temperature, and impact airflow properties [26]. Since coal ash blast intensity characteristics are influenced by different factors, in many cases, changes in certain factors can greatly affect the blast intensity and even change the nature of the blast [27]. In a discrete time signal, if two very close samples have different algebraic signs, they are called over-zero:…”

Section: Svm-based Prediction On Sensitivity To Coalmentioning

confidence: 99%

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

et al. 2022

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Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.

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Section: Svm-based Prediction On Sensitivity To Coalmentioning

confidence: 99%

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

et al. 2022

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“…The developed geodatabase eliminates sources of conflicts in land use or mitigates their impact in areas where metallurgical, mining and chemical production are concentrated. Similarly, in other countries with developed mining and subsequent environmental problems, such as Nigeria (Ochelebe et al, 2020), Morocco (Khalil et al, 2014), China (Qi et al,2017), Indonesia (Maryati et al, 2012), Soth Africa (Orimoloye & Ololade, 2020) Sierra Leone (Kamara, 2020), the use of GIS with combination with modern remote sensing technologies and tools is becoming routine.…”

Section: Introductionmentioning

confidence: 99%

Spatial Database Designing for Environmental Monitoring and Decision Making in Mitrovica Region, The Republic of Kosovo

2021

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The integration of spatial data analysis methods and thematic map models is an approach to reduce the negative impact of anthropogenic pressure on the environment due to mining and waste generation. The large amounts of industrial waste from mining in the Mitrovica region in northern Kosovo lead to serious environmental problems with organic and inorganic water and soil pollution. This study aims to design and establish a geospatial database for long-term environmental monitoring, provide analytical tools, and support appropriate management decisions by local authorities and agencies. The database contains topographical elements and ecological parameters collected from different national and open access international sources. All collected data have been analyzed, standardized and harmonized within the open-source QGIS ver.3 software. The results showed that in developed datasets were organized in different GIS layers and compiled several thematic maps. The designed database is unique by its architecture, providing an opportunity for periodical monitoring of the environment near the mining areas. Keywords: Environmental monitoring; Spatial database; Open source software; QGIS; Kosovo. Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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“…and daily life [1,2] . Therefore, rapid and accurate detection of methane concentration is of great significance.…”

mentioning

confidence: 99%

Research on temperature compensation method for methane sensor based on TDLAS

Zhang,

Gao,

Yao

et al. 2023

AOPC 2023: Optic Fiber Gyro

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Methane, as the main component of gas and natural gas, its flammable and explosive characteristics to industrial production, urban safety and daily life has brought great potential hazards. Accurately measuring the concentration of methane gas is of great significance for urban development and environmental protection. At present, tunable semiconductor laser absorption spectroscopy (TDLAS) has become one of the important technologies for methane sensor concentration measurement. However, this technology is easily affected by environmental temperature, and the relative error of concentration measurement caused by environmental temperature changes is greater than 50%. Therefore, this article proposes a temperature compensation calibration method for methane sensors based on TDLAS technology. This article first analyzes the effect of temperature on the absorption intensity of methane gas, and studies the relationship between its concentration value and temperature. Then, based on theoretical analysis, the negative temperature coefficient variation between the concentration value and temperature is achieved through power normalization processing algorithm. At the same time, a temperature compensation system is established to compensate for the concentration value and suppress the interference caused by environmental temperature to the detection. Finally, the feasibility of the temperature compensation calibration method was verified through experiments. The experimental results show that under the same testing conditions, the relative error of the measured concentration after temperature compensation can be reduced to less than 5%, significantly improving the detection accuracy of the methane sensor.

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Analysis of Risk Management for the Coal Mine Operations

Cited by 3 publications

References 4 publications

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

Spatial Database Designing for Environmental Monitoring and Decision Making in Mitrovica Region, The Republic of Kosovo

Research on temperature compensation method for methane sensor based on TDLAS

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