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The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions—Electricity Prediction Using ANN

Author

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  • Marek Borowski

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Krakow, Poland)

  • Piotr Życzkowski

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Krakow, Poland)

  • Jianwei Cheng

    (Key Laboratory of Gas and Fire Control for Coal Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Rafał Łuczak

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Krakow, Poland)

  • Klaudia Zwolińska

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Krakow, Poland)

Abstract

Greenhouse gases such as carbon dioxide and methane cause global warming and consequently climate change. Great efforts are being made to reduce greenhouse gas emissions with the objective of addressing this problem, hence the popularity of technologies conductive to reducing greenhouse gas emissions. CO 2 emissions can be reduced by improving the thermal efficiency of combustion engines, for example, by using cogeneration systems. Coal mine methane (CMM) emerges due to mining activities as methane released from the coal and surrounding rock strata. The amount of methane produced is primarily influenced by the productivity of the coal mine and the gassiness of the coal seam. The gassiness of the formation around the coal seam and geological conditions are also important. Methane can be extracted to the surface using methane drainage installations and along with ventilation air. The large amounts of methane captured by methane drainage installations can be used for energy production. This article presents a quarterly summary of the hourly values of methane capture, its concentration in the methane–air mixture, and electricity production in the cogeneration system for electricity and heat production. On this basis, neural network models have been proposed in order to predict electricity production based on known values of methane capture, its concentration, pressure, and parameters determining the time and day of the week. A prediction model has been established on the basis of a multilayer perceptron network (MLP).

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4429-:d:404878
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    References listed on IDEAS

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    Cited by:

    1. 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.
    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.
    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.
    4. 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.
    5. Magdalena Tutak & Tibor Krenicky & Rastislav Pirník & Jarosław Brodny & Wiesław Wes Grebski, 2024. "Predicting Methane Concentrations in Underground Coal Mining Using a Multi-Layer Perceptron Neural Network Based on Mine Gas Monitoring Data," Sustainability, MDPI, vol. 16(19), pages 1-21, September.

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