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A Gas Concentration Prediction Method Driven by a Spark Streaming Framework

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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 traditional coal-mine gas-concentration prediction process, problems such as low timeliness of data and low efficiency of the prediction model in learning data features result in low accuracy of the final prediction. To solve these problems, a gas-concentration prediction method driven by the Spark Streaming framework is proposed. In this research study, the Spark Streaming framework, autoregressive integrated moving average (ARIMA) model and support vector machine (SVM) model are used to construct a new prediction model called the SPARS model. The Spark Streaming framework is used to process large batches of real-time streaming data in a short period of time, and the model can be used to intermittently update and optimize the prediction model so that the model can fully learn the characteristics of the data. At the same time, the advantages of the ARIMA model and SVM model for processing linear data and nonlinear data are combined to improve the model’s prediction efficiency and fully reflect the timeliness of gas prediction. Finally, the proposed prediction model is verified using gas data collected on site. The optimal learning time for the SPARS model in predicting this set of data is determined, and a comparative analysis of the prediction results obtained from the ARIMA, SVM and other models fully confirms that high-precision prediction results can be obtained using the SPARS model. The proposed model can be used to realize scientific and accurate real-time prediction and analyses of coal-mine gas concentrations and provides a new idea for realizing real-time and accurate gas prediction in coal mines.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5335-:d:869495
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    References listed on IDEAS

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    1. 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.
    2. Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
    3. Issam Dawoud & Selahattin Kaçiranlar, 2017. "An optimal k of kth MA-ARIMA models under a class of ARIMA model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 5754-5765, June.
    4. Ivan Svetunkov & John E. Boylan, 2020. "State-space ARIMA for supply-chain forecasting," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 818-827, February.
    5. Xiangqian Wang & Ningke Xu & Xiangrui Meng & Haoqian Chang, 2022. "Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    6. Magdalena Tutak & Jarosław Brodny, 2019. "Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks," IJERPH, MDPI, vol. 16(8), pages 1-21, April.
    7. Wang, Ke & Zhang, Jianjun & Cai, Bofeng & Yu, Shengmin, 2019. "Emission factors of fugitive methane from underground coal mines in China: Estimation and uncertainty," Applied Energy, Elsevier, vol. 250(C), pages 273-282.
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    Cited by:

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    2. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

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