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Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process

Author

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  • Magdalena Tutak

    (Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Jarosław Brodny

    (Faculty of Organization and Management, Silesian University of Technology, 41-800 Zabrze, Poland)

Abstract

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

Suggested Citation

  • Magdalena Tutak & Jarosław Brodny, 2019. "Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process," Energies, MDPI, vol. 12(20), pages 1-28, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3840-:d:275168
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    2. Tran, Trung Kien & Lin, Chia-Yang & Tu, Yu-Te & Duong, Nam Tien & Pham Thi, Thuy Dung & Shoh-Jakhon, Khamdamov, 2023. "Nexus between natural resource depletion and rent and COP26 commitments: Empirical evidence from Vietnam," Resources Policy, Elsevier, vol. 85(PB).
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    4. Qianyou Wang & Yaohua Li & Wei Yang & Zhenxue Jiang & Yan Song & Shu Jiang & Qun Luo & Dan Liu, 2019. "Finite Element Simulation of Multi-Scale Bedding Fractures in Tight Sandstone Oil Reservoir," Energies, MDPI, vol. 13(1), pages 1-20, December.
    5. Guo, Jinling & Gao, Junlian & Yan, Kejia & Zhang, Bo, 2023. "Unintended mitigation benefits of China's coal de-capacity policies on methane emissions," Energy Policy, Elsevier, vol. 181(C).
    6. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
    7. Jarosław Brodny & Magdalena Tutak, 2020. "The Use of Artificial Neural Networks to Analyze Greenhouse Gas and Air Pollutant Emissions from the Mining and Quarrying Sector in the European Union," Energies, MDPI, vol. 13(8), pages 1-31, April.
    8. Nan Zhang & Wei Liu & Yun Zhang & Pengfei Shan & Xilin Shi, 2020. "Microscopic Pore Structure of Surrounding Rock for Underground Strategic Petroleum Reserve (SPR) Caverns in Bedded Rock Salt," Energies, MDPI, vol. 13(7), pages 1-22, March.
    9. Hewan Li & Jianping Zuo & Laigui Wang & Pengfei Li & Xiaowei Xu, 2020. "Mechanism of Structural Damage in Low Permeability Coal Material of Coalbed Methane Reservoir under Cyclic Cold Loading," Energies, MDPI, vol. 13(3), pages 1-15, January.
    10. 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.
    11. Marcin Karbownik & Jerzy Krawczyk & Katarzyna Godyń & Tomasz Schlieter & Jiří Ščučka, 2021. "Analysis of the Influence of Coal Petrography on the Proper Application of the Unipore and Bidisperse Models of Methane Diffusion," Energies, MDPI, vol. 14(24), pages 1-20, December.
    12. Dawid Szurgacz & Sergey Zhironkin & Stefan Vöth & Jiří Pokorný & A.J.S. (Sam) Spearing & Michal Cehlár & Marta Stempniak & Leszek Sobik, 2021. "Thermal Imaging Study to Determine the Operational Condition of a Conveyor Belt Drive System Structure," Energies, MDPI, vol. 14(11), pages 1-18, June.
    13. Marcin Karbownik & Agnieszka Dudzińska & Jarosław Strzymczok, 2022. "Multi-Parameter Analysis of Gas Losses Occurring during the Determination of Methane-Bearing Capacity in Hard Coal Beds," Energies, MDPI, vol. 15(9), pages 1-17, April.
    14. Dawid Szurgacz & Sergey Zhironkin & Michal Cehlár & Stefan Vöth & Sam Spearing & Ma Liqiang, 2021. "A Step-by-Step Procedure for Tests and Assessment of the Automatic Operation of a Powered Roof Support," Energies, MDPI, vol. 14(3), pages 1-16, January.
    15. Wang, Qian & Gu, Qinghua & Li, Xuexian & Xiong, Naixue, 2024. "Comprehensive overview: Fleet management drives green and climate-smart open pit mine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. Jarosław Brodny & Magdalena Tutak & Saqib Ahmad Saki, 2020. "Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland," Energies, MDPI, vol. 13(10), pages 1-31, May.

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