尾矿设施危害与风险表征的复杂网络模型

Shuang Gao, Z. Zhen, Zhongxue Li, Yiqing Zhao, Xuan Qin
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引用次数: 3

摘要

如果管理不善,尾矿设施可能成为危险的主要来源,危及社区和环境,并损害矿物工业在可持续性方面的声誉。确定、描述和减轻与尾矿设施有关的危害和风险对维持社区安全和无害环境的尾矿设施至关重要。在此基础上,建立了一个复杂的网络模型,用于表征尾矿设施生命周期相关的危害和风险。在这种方法中,危险被建模为复杂网络的顶点,危险之间的相互作用被建模为复杂网络的边。利用网络密度、几何特征、特征路径长度、网络效率和聚类系数等网络指标,对尾矿库危害与风险扩散建模的复杂网络进行了分析和表征。网络的度分布服从幂律分布,表明表征尾矿库风险扩散的网络是无标度的。根据计算结果和已有的研究成果,网络是超小世界的。通过分析四种不同的网络节点和边缘去除方法对全局网络效率的影响,将中间度(BC)较高的网络节点识别为关键节点。移除这些关键节点有助于降低与尾矿设施相关的风险,并揭示了对BC攻击的脆弱性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complex Network Model for Characterizing Hazards and Risks Associated with Mine-tailings Facility
If not well-managed, a mine-tailings facility may become a major source of risks, endangering the community and environment, and damaging the reputation of the minerals industry regarding sustainability. Identifying, characterizing, and mitigating the hazards and risks associated with tailings facilities have been critical to the maintenance of community-safe and environmentally sound mine-tailings facilities. Herein, a complex network model for characterizing the hazards and risks associated with the lifecycle of tailings facilities is presented. In this approach, the hazards are modeled as vertices of the complex network, and the interactions among the hazards are modeled as edges of the complex network. The complex network for modeling the hazard and risk spreading of mine-tailings impoundments is analyzed and characterized by using network metrics such as the network density, geometrical characteristics, characteristic path length, network efficiency, and clustering coefficient. The degree distribution of the network obeys a power-law distribution, indicating that the network for characterizing the risk spreading associated with a tailings facility is scale-free. According to the results of calculations and existing research results, the network is ultrasmall-world. By analyzing the change of the global network efficiency under four kinds of different methods to remove network nodes and edges, network nodes with higher between centrality (BC) are identified as critical. The removal of those critical nodes helps mitigate risks associated with a tailings facility and reveals the vulnerabilities to BC attacks.
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