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Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region

基于云聚类分析的高寒高海拔地区地下金属矿山通风系统评估

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Abstract

Ventilation system is significant in underground metal mine of alpine region. Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system. However, it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region. This paper proposes a novel evaluation method called the cloud model-clustering analysis (CMCA). Cloud model (CM) is utilized to process collected data of ventilation system, and they are converted into cloud descriptors by CM. Cloud similarity (CS) based Euclidean distance (ED) is proposed to make clustering analysis of assessed samples. Then the classification of assessed samples will be identified by clustering analysis results. A case study is developed based on CMCA. Evaluation results show that ventilation effectiveness can be well classified. Moreover, CM is used alone to make comparison of evaluation results obtained by CMCA. Then the availability and validity of CMCA is verified. Meanwhile, difference of CS based ED and classical ED is analyzed. Two new clustering analysis methods are introduced to make comparison with CMCA. Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.

摘要

良好的通风系统对于高寒高海拔地下金属矿山意义重大, 科学合理的评估可有效维护风机系统的性能。由于相关分类方法与标准的缺失, 使得高寒高海拔地下金属矿山的风机系统评估难以进行, 因此提出了一种名为云聚类分析的新评估方法。该方法首先采用云模型对风机系统待评估的数据进行前处理, 并将它们转换为云描述符; 然后, 提出一种基于云相似度的欧氏距离并推导出其对应的算法, 以此对各评估样本进行聚类分析; 最后, 根据聚类分析结果对评估样本进行科学分类, 从而完成通风系统的评估。本文引入一个具体实例进行案例分析, 分析结果显示云聚类分析法可以对风机效能进行有效分类, 进而为通风系统的维护管理提供指导。此外, 将单独采用云模型的评估结果与云聚类分析结果进行对比, 验证云聚类分析的可用性与有效性。同时, 对比分析传统欧式距离与基于云相似度的欧式距离, 以及引入两种现有的聚类分析方法与本文所提方法进行比较研究, 结果说明云聚类分析能够满足高海拔高寒地下金属矿山通风系统的评估需求。

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Fang Yan  (闫放), Zi-jun Li  (李孜军), Long-jun Dong  (董陇军), Rui Huang  (黄锐) & Ri-hong Cao  (曹日红)

  2. Safety & Security Theory Innovation and Promotion Center (STIPC), Central South University, Changsha, 410083, China

    Fang Yan  (闫放), Zi-jun Li  (李孜军), Long-jun Dong  (董陇军) & Rui Huang  (黄锐)

  3. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, 110819, China

    Ji Ge  (葛及) & Kai-li Xu  (许开立)

  4. School of Resources and Environmental Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China

    Ji Ge  (葛及)

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  1. Fang Yan  (闫放)
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  6. Ji Ge  (葛及)
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Correspondence to Zi-jun Li  (李孜军).

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Foundation item

Project(2018YFC0808404) supported by National Key Research and Development Program of China

Contributors

YAN Fang, HUANG Rui and GE Ji developed the overarching research goals. YAN Fang, LI Zi-jun and CAO Ri-hong provided the concept and edited the draft of manuscript. YAN Fang conducted the literature review and wrote the first draft of the manuscript. LI Zi-jun and XU Kai-li edited the draft of manuscript. DONG Long-jun and YAN Fang revised the manuscript.

Conflict of interest

YAN Fang, LI Zi-jun, DONG Long-jun, HUANG Rui, CAO Ri-hong, GE Ji and XU Kai-li declare that they have no conflict of interest.

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Yan, F., Li, Zj., Dong, Lj. et al. Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region. J. Cent. South Univ. 28, 796–815 (2021). https://doi.org/10.1007/s11771-021-4646-5

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