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Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model

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  • Yongkang Yang

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
    State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology (Beijing), Beijing 100083, China)

  • Qiaoyi Du

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Chenlong Wang

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Yu Bai

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6112-:d:448977
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    References listed on IDEAS

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    Cited by:

    1. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    2. Lin, Haifei & Li, Wenjing & Li, Shugang & Wang, Lin & Ge, Jiaqi & Tian, Yu & Zhou, Jie, 2024. "Coal mine gas emission prediction based on multifactor time series method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    3. 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.

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