CIESC Journal ›› 2023, Vol. 74 ›› Issue (4): 1639-1650.DOI: 10.11949/0438-1157.20221618

• Process system engineering • Previous Articles     Next Articles

Deep-mining risk evolution path of chemical processes based on community structure

Cheng YUN1(), Qianlin WANG1(), Feng CHEN2, Xin ZHANG3, Zhan DOU1, Tingjun YAN1   

  1. 1.College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
    2.College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
    3.Instrumentation Technology and Economy Institute, Beijing 100055, China
  • Received:2022-12-13 Revised:2023-02-03 Online:2023-06-02 Published:2023-04-05
  • Contact: Qianlin WANG


贠程1(), 王倩琳1(), 陈锋2, 张鑫3, 窦站1, 颜廷俊1   

  1. 1.北京化工大学机电工程学院,北京 100029
    2.中国石油大学(北京)机械与储运工程学院,北京 102249
    3.机械工业仪器仪表综合技术经济研究所,北京 100055
  • 通讯作者: 王倩琳
  • 作者简介:贠程(1996—),男,硕士研究生,
  • 基金资助:


In the process of chemical production, due to the danger of materials and the complexity of the process, serious accidents occur frequently. It is necessary to reveal risk evolution mechanism to ensure the safe and stable operation of chemical processes, as well as to inhibit the spread, diffusion, or even evolution of secondary and derivative disasters. However, many traditional methods heavily rely on expert experience or prior information, which will cause the inaccuracy of risk assessment results. Although the community structure in complex network can be regarded as a highly abstract of risk evolution path, the existing algorithms cannot consider both of the rationality and accuracy division results. Therefore, a deep mining method of risk evolution path is proposed for chemical processes based on the DSAE-Louvain community structure. First, multi-source process data should be processed to establish a risk evolution network, and the Dijkstra and hop-count algorithms are further applied to obtain a similarity matrix. Then, the deep sparse autoencoder (DSAE) and Louvain algorithm are combined to divide the community structure using sparse analysis. Finally, the risk weak nodes and critical evolution paths in whole chemical processes are deeply mined according to the ranking of node importance. To illustrate its validity, the Tennessee Eastman (TE) process is selected as a test case. The results show that the proposed DSAE-Louvain method are more refined and high-efficiency in the community structure division by comparing with the GN algorithm, Louvain algorithm, and DSAE-GN method. Particularly, the mined risk evolution paths are more in accord with actual production processes.

Key words: chemical processes, risk evolution, path mining, process systems, neural networks, safety


在化工生产过程中,由于物料的危险性和流程的复杂性,导致恶性事故频发。为了保证化工过程的安全平稳运行,需对其风险演化机制进行深入解析,以抑制次衍生灾害事故的传播、扩散及演变。然而,传统方法过于依赖专家经验或先验信息,风险评估结果不精准;以复杂网络为基础的社团结构虽可视为风险演化路径的高度抽象,但已有算法难以兼顾划分结果的合理性和准确率。为此,提出一种基于DSAE-Louvain社团结构的化工过程风险演化路径深度挖掘方法。首先对多源过程数据进行处理,构建风险演化网络模型,同时利用Dijkstra算法、跳数法等手段,获取相似度矩阵;进而引入深度稀疏自编码器(DSAE)与Louvain算法,经稀疏处理开展社团结构划分;最后,根据节点重要度排序追踪整个化工过程的风险薄弱节点和关键演化路径。以Tennessee Eastman (TE)过程为例,对比GN算法、Louvain算法和DSAE-GN方法,结果表明DSAE-Louvain方法能够提升社团结构划分的精细化、高效化程度,且所挖掘的风险演化路径更为符合实际生产工艺流程。

关键词: 化工过程, 风险演化, 路径挖掘, 过程系统, 神经网络, 安全

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