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Calibration of Mine Ventilation Network Models Using the Non-Linear Optimization Algorithm

Author

Listed:
  • Guang Xu

    (Department of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Curtin University, Kalgoorlie 6430, Australia
    State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221100, China)

  • Jinxin Huang

    (Department of Mining Engineering and Metallurgical Engineering, Western Australian School of Mines, Curtin University, Kalgoorlie 6430, Australia)

  • Baisheng Nie

    (School of Resources and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Duncan Chalmers

    (School of Mining Engineering, University of New South Wales, Sydney 2052, Australia)

  • Zhuoming Yang

    (School of Resources and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

Effective ventilation planning is vital to underground mining. To ensure stable operation of the ventilation system and to avoid airflow disorder, mine ventilation network (MVN) models have been widely used in simulating and optimizing the mine ventilation system. However, one of the challenges for MVN model simulation is that the simulated airflow distribution results do not match the measured data. To solve this problem, a simple and effective calibration method is proposed based on the non-linear optimization algorithm. The calibrated model not only makes simulated airflow distribution results in accordance with the on-site measured data, but also controls the errors of other parameters within a minimum range. The proposed method was then applied to calibrate an MVN model in a real case, which is built based on ventilation survey results and Ventsim software. Finally, airflow simulation experiments are carried out respectively using data before and after calibration, whose results were compared and analyzed. This showed that the simulated airflows in the calibrated model agreed much better to the ventilation survey data, which verifies the effectiveness of calibrating method.

Suggested Citation

  • Guang Xu & Jinxin Huang & Baisheng Nie & Duncan Chalmers & Zhuoming Yang, 2017. "Calibration of Mine Ventilation Network Models Using the Non-Linear Optimization Algorithm," Energies, MDPI, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:31-:d:124184
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    Citations

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    Cited by:

    1. Xu Huang & Yunlong Liu, 2022. "Research and design of intelligent mine ventilation construction architecture [Current situation and intelligent development prospect of mine ventilation technology in my country]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1232-1238.
    2. Zi-shan Gao & Chuan-jie Zhu & Xi-miao Lu & Jie Ren, 2020. "Prevention and control of abnormal gas emission caused by accidental discharge of floor fissure water: a case study," 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. 100(2), pages 713-733, January.

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