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Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network

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  • Ming Liu
  • Liping Wu
  • Mingjun Hou

Abstract

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

Suggested Citation

  • Ming Liu & Liping Wu & Mingjun Hou, 2024. "Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0302044
    DOI: 10.1371/journal.pone.0302044
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    References listed on IDEAS

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    1. Gong, Yan & Zhang, Qing & Zhu, Huiwen & Guo, Qinghua & Yu, Guangsuo, 2017. "Refractory failure in entrained-flow gasifier: Vision-based macrostructure investigation in a bench-scale OMB gasifier," Applied Energy, Elsevier, vol. 205(C), pages 1091-1099.
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