IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v170y2019icp777-790.html
   My bibliography  Save this article

Fire risk prevention in underground coal gasification (UCG) within active mines: Temperature forecast by means of MARS models

Author

Listed:
  • Krzemień, Alicja

Abstract

This paper focuses on fire prevention in UCG processes within active mines by means of temperature forecasting by a multivariate adaptive regression splines (MARS) approach. The main aim was to develop a model to forecast the temperature of the syngas with one hour of anticipation based on information from different parameters measured every hour (snapshots) during the experiment. As the response time of the syngas temperature to modifications in the composition/amounts of the gasifying agent is very short, this will reduce the temperature if necessary while keeping it as high as possible within the safety parameters, as UCG is a strongly exothermic process. The same model can be used to prevent undesired drops in the temperature of the syngas, as low temperatures could increase the precipitation of contaminants, causing a slowdown in the syngas flow and thus decreasing its calorific value.

Suggested Citation

  • Krzemień, Alicja, 2019. "Fire risk prevention in underground coal gasification (UCG) within active mines: Temperature forecast by means of MARS models," Energy, Elsevier, vol. 170(C), pages 777-790.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:777-790
    DOI: 10.1016/j.energy.2018.12.179
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218325532
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.12.179?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christopher Otto & Thomas Kempka, 2017. "Prediction of Steam Jacket Dynamics and Water Balances in Underground Coal Gasification," Energies, MDPI, vol. 10(6), pages 1-17, May.
    2. Laciak, Marek & Kostúr, Karol & Durdán, Milan & Kačur, Ján & Flegner, Patrik, 2016. "The analysis of the underground coal gasification in experimental equipment," Energy, Elsevier, vol. 114(C), pages 332-343.
    3. Akihiro Hamanaka & Fa-qiang Su & Ken-ichi Itakura & Kazuhiro Takahashi & Jun-ichi Kodama & Gota Deguchi, 2017. "Effect of Injection Flow Rate on Product Gas Quality in Underground Coal Gasification (UCG) Based on Laboratory Scale Experiment: Development of Co-Axial UCG System," Energies, MDPI, vol. 10(2), pages 1-11, February.
    4. Mocek, Piotr & Pieszczek, Marek & Świądrowski, Jerzy & Kapusta, Krzysztof & Wiatowski, Marian & Stańczyk, Krzysztof, 2016. "Pilot-scale underground coal gasification (UCG) experiment in an operating Mine “Wieczorek” in Poland," Energy, Elsevier, vol. 111(C), pages 313-321.
    5. Javier DE ANDRES & Fernando SÁNCHEZ-LASHERAS & Pedro LORCA & Francisco Javier DE COS JUEZ, 2011. "A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 10(3), pages 351-374, September.
    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. Milan Durdán & Marta Benková & Marek Laciak & Ján Kačur & Patrik Flegner, 2021. "Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion," Energies, MDPI, vol. 14(17), pages 1-28, September.
    2. Aleksander Frejowski & Jan Bondaruk & Adam Duda, 2021. "Challenges and Opportunities for End-of-Life Coal Mine Sites: Black-to-Green Energy Approach," Energies, MDPI, vol. 14(5), pages 1-18, March.
    3. Majid Mohammady, 2023. "Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 703-721, May.
    4. Yanpeng Chen & Tianduoyi Wang & Jinhua Zhang & Mengyuan Zhang & Junjie Xue & Juntai Shi & Yongshang Kang & Shengjie Li, 2022. "Simulation of Water Influx and Gasified Gas Transport during Underground Coal Gasification with Controlled Retracting Injection Point Technology," Energies, MDPI, vol. 15(11), pages 1-29, May.
    5. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    6. Li, Xin & Tian, Jijun & Ju, Yiwen & Chen, Yanpeng, 2022. "Permeability variations of lignite and bituminous coals under elevated pyrolysis temperatures (35–600 °C): An experimental study," Energy, Elsevier, vol. 254(PA).
    7. Chen, Liangzhou & Qi, Xuyao & Zhang, Yabo & Rao, Yuxuan & Wang, Tao, 2022. "Gasification characteristics and thermodynamic analysis of ultra-lean oxygen oxidized lignite residues," Energy, Elsevier, vol. 240(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. Stefan Zelenak & Erika Skvarekova & Andrea Senova & Gabriel Wittenberger, 2021. "The Usage of UCG Technology as Alternative to Reach Low-Carbon Energy," Energies, MDPI, vol. 14(13), pages 1-15, June.
    2. Wang, Xiaorui & Zhang, Qinghe & Yuan, Liang, 2024. "A coupled thermal-force-chemical-displacement multi-field model for underground coal gasification based on controlled retraction injection point technology and its thermal analysis," Energy, Elsevier, vol. 293(C).
    3. Kumari, Geeta & Vairakannu, Prabu, 2018. "CO2-air based two stage gasification of low ash and high ash Indian coals in the context of underground coal gasification," Energy, Elsevier, vol. 143(C), pages 822-832.
    4. Fa-qiang Su & Akihiro Hamanaka & Ken-ichi Itakura & Gota Deguchi & Wenyan Zhang & Hua Nan, 2018. "Evaluation of a Compact Coaxial Underground Coal Gasification System Inside an Artificial Coal Seam," Energies, MDPI, vol. 11(4), pages 1-11, April.
    5. Xin, Lin & An, Mingyu & Feng, Mingze & Li, Kaixuan & Cheng, Weimin & Liu, Weitao & Hu, Xiangming & Wang, Zhigang & Han, Limin, 2021. "Study on pyrolysis characteristics of lump coal in the context of underground coal gasification," Energy, Elsevier, vol. 237(C).
    6. Xi Lin & Qingya Liu & Zhenyu Liu, 2018. "Estimation of Effective Diffusion Coefficient of O 2 in Ash Layer in Underground Coal Gasification by Thermogravimetric Apparatus," Energies, MDPI, vol. 11(2), pages 1-14, February.
    7. Karol Kostúr & Marek Laciak & Milan Durdan, 2018. "Some Influences of Underground Coal Gasification on the Environment," Sustainability, MDPI, vol. 10(5), pages 1-31, May.
    8. Natalie Nakaten & Thomas Kempka, 2019. "Techno-Economic Comparison of Onshore and Offshore Underground Coal Gasification End-Product Competitiveness," Energies, MDPI, vol. 12(17), pages 1-28, August.
    9. Ding, Kangle & Zhang, Changmin, 2017. "Interactions between organic nitrogen and inorganic matter in the pyrolysis zone of underground coal gasification: Insights from controlled pyrolysis experiments," Energy, Elsevier, vol. 135(C), pages 279-293.
    10. Ján Kačur & Marek Laciak & Milan Durdán & Patrik Flegner, 2023. "Investigation of Underground Coal Gasification in Laboratory Conditions: A Review of Recent Research," Energies, MDPI, vol. 16(17), pages 1-55, August.
    11. Mohammadreza Shahbazi & Mehdi Najafi & Mohammad Fatehi Marji, 2019. "On the mitigating environmental aspects of a vertical well in underground coal gasification method," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(3), pages 373-398, March.
    12. Carlo Caserio & Delio Panaro & Sara Trucco, 2014. "A statistical analysis of reliability of audit opinions as bankruptcy predictors," Discussion Papers 2014/174, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    13. Hongtao Liu & Feng Chen & Yuanyuan Wang & Gang Liu & Hong Yao & Shuqin Liu, 2018. "Experimental Study of Reverse Underground Coal Gasification," Energies, MDPI, vol. 11(11), pages 1-13, October.
    14. Marek Laciak & Milan Durdán & Ján Kačur & Patrik Flegner, 2023. "The Underground Coal Gasification Process in Laboratory Conditions: An Experimental Study," Energies, MDPI, vol. 16(7), pages 1-19, April.
    15. Zhang, Nan & Zhang, Jianliang & Wang, Guangwei & Ning, Xiaojun & Meng, Fanyi & Li, Chuanhui & Ye, Lian & Wang, Chuan, 2022. "Physicochemical characteristics of three-phase products of low-rank coal by hydrothermal carbonization: experimental research and quantum chemical calculation," Energy, Elsevier, vol. 261(PB).
    16. Feng, Lele & Zhou, Sibo & Xu, Xiangcen & Qin, Botao, 2022. "Importance evaluation for influencing factors of underground coal gasification through ex-situ experiment and analytic hierarchy process," Energy, Elsevier, vol. 261(PA).
    17. Jacek Borgulat & Katarzyna Ponikiewska & Łukasz Jałowiecki & Aleksandra Strugała-Wilczek & Grażyna Płaza, 2022. "Are Wetlands as an Integrated Bioremediation System Applicable for the Treatment of Wastewater from Underground Coal Gasification Processes?," Energies, MDPI, vol. 15(12), pages 1-19, June.
    18. Czekała, Wojciech & Bartnikowska, Sylwia & Dach, Jacek & Janczak, Damian & Smurzyńska, Anna & Kozłowski, Kamil & Bugała, Artur & Lewicki, Andrzej & Cieślik, Marta & Typańska, Dorota & Mazurkiewicz, Ja, 2018. "The energy value and economic efficiency of solid biofuels produced from digestate and sawdust," Energy, Elsevier, vol. 159(C), pages 1118-1122.
    19. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    20. Krzysztof Kapusta, 2021. "Effect of Lignite Properties on Its Suitability for the Implementation of Underground Coal Gasification (UCG) in Selected Deposits," Energies, MDPI, vol. 14(18), pages 1-11, September.

    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:eee:energy:v:170:y:2019:i:c:p:777-790. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.