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Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

Author

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  • Yuxin Huang

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Jingdao Fan

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Zhenguo Yan

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Shugang Li

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Yanping Wang

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k -means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k -means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

Suggested Citation

  • Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2021. "Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining," Energies, MDPI, vol. 14(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6889-:d:661091
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    References listed on IDEAS

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    1. Izabela Jonek-Kowalska, 2018. "Method for Assessing the Development of Underground Hard Coal Mines on a Regional Basis: The Concept of Measurement and Research Results," Energies, MDPI, vol. 11(6), pages 1-23, May.
    2. Yongkang Yang & Qiaoyi Du & Chenlong Wang & Yu Bai, 2020. "Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model," Energies, MDPI, vol. 13(22), pages 1-15, November.
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    4. Marek Borowski & Piotr Życzkowski & Klaudia Zwolińska & Rafał Łuczak & Zbigniew Kuczera, 2021. "The Security of Energy Supply from Internal Combustion Engines Using Coal Mine Methane—Forecasting of the Electrical Energy Generation," Energies, MDPI, vol. 14(11), pages 1-18, May.
    5. Marek Borowski & Piotr Życzkowski & Jianwei Cheng & Rafał Łuczak & Klaudia Zwolińska, 2020. "The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions—Electricity Prediction Using ANN," Energies, MDPI, vol. 13(17), pages 1-18, August.
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    7. Nikodem Szlązak & Justyna Swolkień, 2021. "Possibilities of Capturing Methane from Hard Coal Deposits Lying at Great Depths," Energies, MDPI, vol. 14(12), pages 1-19, June.
    8. Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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    Cited by:

    1. Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2022. "A Gas Concentration Prediction Method Driven by a Spark Streaming Framework," Energies, MDPI, vol. 15(15), pages 1-13, July.
    2. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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