IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8495-d704058.html
   My bibliography  Save this article

Analysis of the Influence of Coal Petrography on the Proper Application of the Unipore and Bidisperse Models of Methane Diffusion

Author

Listed:
  • Marcin Karbownik

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

  • Jerzy Krawczyk

    (The Strata Mechanics Research Institute of Polish Academy of Sciences, Reymonta 27, 30-059 Krakow, Poland)

  • Katarzyna Godyń

    (The Strata Mechanics Research Institute of Polish Academy of Sciences, Reymonta 27, 30-059 Krakow, Poland)

  • Tomasz Schlieter

    (Department of Computational Mechanics and Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

  • Jiří Ščučka

    (Institute of Geonics of the CAS, Studentská 1768, 708 00 Ostrava, Czech Republic)

Abstract

The analysis of phenomena related to gas transport in hard coal is important with regard to the energetic use of coal bed methane (CBM), the reduction of greenhouse gas emissions to the atmosphere (CO 2 ) and the prevention of natural hazards such as methane hazards and gas and rock outbursts. This article presents issues concerning the feasibility and scope of applying the unipore and bidisperse diffusion models to obtain knowledge concerning the kinetics of methane sorption and its diffusion in the carbon structure, depending on its petrography. Laboratory tests were carried out on coal samples which varied in terms of petrography. Quantitative point analyses were carried out, based on which content of groups of macerals was determined. The degree of coalification of coal samples was also determined based on measurements of vitrinite reflectivity R 0 and the volatile matter content V daf . Sorption kinetics were also investigated, and attempts were made to adjust the unipore and bidisperse models to the real sorption kinetic courses. This allowed the identification of appropriate coefficients controlling the course of sorption in mathematical models. An attempt was also made to assess the possibility of applying a given model to properly describe the phenomenon of methane sorption on hard coal.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8495-:d:704058
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8495/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8495/
    Download Restriction: no
    ---><---

    References listed on IDEAS

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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).

    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. 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.
    3. 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.
    4. Boris V. Malozyomov & Vladimir Ivanovich Golik & Vladimir Brigida & Vladislav V. Kukartsev & Yadviga A. Tynchenko & Andrey A. Boyko & Sergey V. Tynchenko, 2023. "Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors," Energies, MDPI, vol. 16(11), pages 1-16, May.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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).
    14. 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.
    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. 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.
    17. 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.
    18. 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:14:y:2021:i:24:p:8495-:d:704058. 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.