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An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines

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  • Boyu Luan

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Zhou

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Izhar Mithal Jiskani

    (Department of Mining Engineering, National University of Sciences & Technology, Balochistan Campus, Quetta 87300, Pakistan)

  • Zhiming Wang

    (State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
    School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 μg/m 3 for PM2.5, from 15.73 to 5.28 μg/m 3 for PM10, and from 18.99 to 6.27 μg/m 3 for TSP, and the Pearson correlation coefficient and the mean absolute error also improved considerably. This work provides an improved machine learning approach for dust concentration estimation in open-pit coal mines, with a greater emphasis on simplicity and rapid model updates, which is more applicable to ensure the prudent use of water resources and overall environmental conservation, both of which are advantageous to green mining.

Suggested Citation

  • Boyu Luan & Wei Zhou & Izhar Mithal Jiskani & Zhiming Wang, 2023. "An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1353-:d:1032740
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    References listed on IDEAS

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    Cited by:

    1. Zhichao Liu & Zhongchen Ao & Wei Zhou & Baowei Zhang & Jingfu Niu & Zhiming Wang & Lijie Liu & Zexuan Yang & Kun Xu & Wenqi Lu & Lixia Zhu, 2023. "Research on the Physical and Chemical Characteristics of Dust in Open Pit Coal Mine Crushing Stations and Closed Dust Reduction Methods," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    2. 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).
    3. Zhongchen Ao & Zhiming Wang & Wei Zhou & Yanzhen Qiao & Abdoul Wahab & Zexuan Yang & Shouhu Nie & Zhichao Liu & Lixia Zhu, 2023. "CFD Simulation Based Ventilation and Dust Reduction Strategy for Large Scale Enclosed Spaces in Open Pit Coal Mines—A Case of Coal Shed," Sustainability, MDPI, vol. 15(15), pages 1-21, July.

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