IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1036-d726980.html
   My bibliography  Save this article

Numerical Investigation of Roof Stability in Longwall Face Developed in Shallow Depth under Weak Geological Conditions

Author

Listed:
  • Pisith Mao

    (Key Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education of China, School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    Laboratory of Rock Engineering and Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 819-0395, Japan
    Materials Science and Structure Unit, Research and Innovation Center, Institute of Technology of Cambodia, Phnom Penh 12150, Cambodia)

  • Hiroto Hashikawa

    (Laboratory of Rock Engineering and Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Takashi Sasaoka

    (Laboratory of Rock Engineering and Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Hideki Shimada

    (Laboratory of Rock Engineering and Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Zhijun Wan

    (Key Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education of China, School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Akihiro Hamanaka

    (Laboratory of Rock Engineering and Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 819-0395, Japan)

  • Jiro Oya

    (Business Planning Department, Mitsui Matsushima Holdings Co., Ltd., Fukuoka 810-8527, Japan)

Abstract

Developing longwall mining under weak geological conditions imposes a substantial challenge with regard to the higher risk of falling roofs. Maintaining the stability of the longwall face in this aforementioned condition is crucial for smooth operation. Investigating roof conditions in longwall requires detailed study of rock behavior in response to a few key influences. This paper presents the outcome of a numerical analysis of roof stability in shallow depth longwall face under weak geological conditions. A series of validated FLAC3D models was developed to examine the roof condition of the longwall face under the influence of shield canopy ratio, shield resistance force, and stress ratio. The results show that these three key factors have a significant impact on longwall roof conditions, which can be used to optimize its stability. Two distinct mechanisms of the roof caving behavior can be observed under the influence of stress ratio. The countermeasures of reducing face-to-tip distance and cutting width are proposed to improve the roof condition of longwall face under weak rock. The outcomes show a substantial improvement in roof conditions after adopting the proposed method.

Suggested Citation

  • Pisith Mao & Hiroto Hashikawa & Takashi Sasaoka & Hideki Shimada & Zhijun Wan & Akihiro Hamanaka & Jiro Oya, 2022. "Numerical Investigation of Roof Stability in Longwall Face Developed in Shallow Depth under Weak Geological Conditions," Sustainability, MDPI, vol. 14(3), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1036-:d:726980
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/3/1036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, 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. Hiroto Hashikawa & Pisith Mao & Takashi Sasaoka & Akihiro Hamanaka & Hideki Shimada & Ulaankhuu Batsaikhan & Jiro Oya, 2022. "Numerical Simulation on Pillar Design for Longwall Mining under Weak Immediate Roof and Floor Strata in Indonesia," Sustainability, MDPI, vol. 14(24), pages 1-13, December.

    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. He, Liuyue & Xu, Zhenci & Wang, Sufen & Bao, Jianxia & Fan, Yunfei & Daccache, Andre, 2022. "Optimal crop planting pattern can be harmful to reach carbon neutrality: Evidence from food-energy-water-carbon nexus perspective," Applied Energy, Elsevier, vol. 308(C).
    2. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    3. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    4. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    5. Zhen-Yao Chen & R. J. Kuo, 2019. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1137-1154, March.
    6. Golberg, Alexander, 2015. "Environmental exergonomics for sustainable design and analysis of energy systems," Energy, Elsevier, vol. 88(C), pages 314-321.
    7. Xu, Qingyang & Sun, Feihu & Cai, Qiran & Liu, Li-Jing & Zhang, Kun & Liang, Qiao-Mei, 2022. "Assessment of the influence of demand-side responses on high-proportion renewable energy system: An evidence of Qinghai, China," Renewable Energy, Elsevier, vol. 190(C), pages 945-958.
    8. Shen, Peiliang & Jiang, Yi & Zhang, Yangyang & Liu, Songhui & Xuan, Dongxing & Lu, Jianxin & Zhang, Shipeng & Poon, Chi Sun, 2023. "Production of aragonite whiskers by carbonation of fine recycled concrete wastes: An alternative pathway for efficient CO2 sequestration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    10. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    11. Wang, Lian & Yao, Yuedong & Wang, Kongjie & Adenutsi, Caspar Daniel & Zhao, Guoxiang & Lai, Fengpeng, 2022. "Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs," Energy, Elsevier, vol. 243(C).
    12. Bermúdez, Alfredo & Shabani, Mohsen, 2022. "Numerical simulation of gas composition tracking in a gas transportation network," Energy, Elsevier, vol. 247(C).
    13. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    14. Zheng, Peng & Xia, Yucheng & Yao, Tingwei & Jiang, Xu & Xiao, Peiyao & He, Zexuan & Zhou, Desheng, 2022. "Formation mechanisms of hydraulic fracture network based on fracture interaction," Energy, Elsevier, vol. 243(C).
    15. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    16. Miaomiao Tao & Pierre Failler & Lim Thye Goh & Wee Yeap Lau & Hanghang Dong & Liang Xie, 2022. "Quantify the Effect of China’s Emission Trading Scheme on Low-carbon Eco-efficiency: Evidence from China’s 283 Cities," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(6), pages 1-33, August.
    17. Kassouri, Yacouba & Altuntaş, Mehmet & Alola, Andrew Adewale, 2022. "The contributory capacity of natural capital to energy transition in the European Union," Renewable Energy, Elsevier, vol. 190(C), pages 617-629.
    18. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.
    19. Xu, Mengmeng & Lin, Boqiang & Wang, Siquan, 2021. "Towards energy conservation by improving energy efficiency? Evidence from China’s metallurgical industry," Energy, Elsevier, vol. 216(C).
    20. Yu, Peng, 2022. "Posterior probability-based hydraulic unit division and prediction: A case study," Energy, Elsevier, vol. 246(C).

    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:jsusta:v:14:y:2022:i:3:p:1036-:d:726980. 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.