IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3239-d804695.html
   My bibliography  Save this article

Multi-Parameter Analysis of Gas Losses Occurring during the Determination of Methane-Bearing Capacity in Hard Coal Beds

Author

Listed:
  • Marcin Karbownik

    (Department of Mining Aerology, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland)

  • Agnieszka Dudzińska

    (Department of Mining Aerology, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland)

  • Jarosław Strzymczok

    (Department of Mining Aerology, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland)

Abstract

The content of natural methane in hard coal seams, called methane-bearing capacity, is the basic parameter that enables the level of methane hazard to be determined in hard coal mines. This parameter is also used to determine the potential quantities of methane that can be collected and used for energy purposes and the amount of its harmful emission to the atmosphere. Direct methods are most often used to determine methane-bearing capacity. An important aspect that has a great influence on the final result of the research is the gas losses generated at the stage of sampling. Under the conditions of the Polish mining industry, the direct drill cuttings method is used to determine the methane-bearing capacity. Gas losses are compensated for in this method with the use of the coefficient 1.12, by which the obtained result is multiplied. In this paper, a multi-parameter analysis of gas loss in the determination of methane-bearing capacity in hard coal seams has been carried out. Several experiments were performed to identify the most important aspects to be taken into account to obtain a correct result. A methane-bearing capacity test was conducted using two direct methods: the direct drill cuttings method, otherwise known as the single-phase vacuum degassing method, and a method based on the United States Bureau of Mines standards. Sorption studies, such as methane sorption kinetics tests, were also conducted in which sorption properties, such as sorption capacity, effective diffusion coefficient, and half sorption time, were determined. Methane sorption isotherms were also determined, and pore structure was analysed. Based on the obtained test results, an analysis was carried out which made it possible to present appropriate conclusions concerning the gas losses during the methane-bearing capacity test, generated at the stage of sampling. The final result of the work was the proposal of a new gas loss coefficient for the direct drill cuttings method of methane-bearing capacity determination.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3239-:d:804695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3239/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/9/3239/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wang, Kai & Wang, Yanhai & Xu, Chao & Guo, Haijun & Xu, Zhiyuan & Liu, Yifu & Dong, Huzi & Ju, Yang, 2023. "Modeling of multi-field gas desorption-diffusion in coal: A new insight into the bidisperse model," Energy, Elsevier, vol. 267(C).
    3. 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.
    4. 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.
    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. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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).
    12. 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.
    13. 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).
    14. Zetian Zhang & Ru Zhang & Zhiguo Cao & Mingzhong Gao & Yong Zhang & Jing Xie, 2020. "Mechanical Behavior and Permeability Evolution of Coal under Different Mining-Induced Stress Conditions and Gas Pressures," Energies, MDPI, vol. 13(11), pages 1-26, May.
    15. 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.
    16. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3239-:d:804695. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.