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Rule-based expert system to assess caving output ratio in top coal caving

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  • HaiYan Jiang
  • Qinghui Song
  • Kuidong Gao
  • QingJun Song
  • XieGuang Zhao

Abstract

Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.

Suggested Citation

  • HaiYan Jiang & Qinghui Song & Kuidong Gao & QingJun Song & XieGuang Zhao, 2020. "Rule-based expert system to assess caving output ratio in top coal caving," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0238138
    DOI: 10.1371/journal.pone.0238138
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    References listed on IDEAS

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    1. Blaifi, Sid-ali & Moulahoum, Samir & Taghezouit, Bilal & Saim, Abdelhakim, 2019. "An enhanced dynamic modeling of PV module using Levenberg-Marquardt algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 745-760.
    2. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    3. QingJun Song & HaiYan Jiang & Qinghui Song & XieGuang Zhao & Xiaoxuan Wu, 2017. "Combination of minimum enclosing balls classifier with SVM in coal-rock recognition," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
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    Cited by:

    1. Yuming Huo & Dangwei Zhao & Defu Zhu & Zhonglun Wang, 2024. "Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine," Sustainability, MDPI, vol. 16(10), pages 1-18, May.

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