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过程工程学报 ›› 2024, Vol. 24 ›› Issue (3): 371-380.DOI: 10.12034/j.issn.1009-606X.223202

• 研究论文 • 上一篇    

基于报警日志的炼化过程风险薄弱点深度挖掘

朱英齐1,2， 王倩琳1,2*， 张东胜1,2， 窦站1,2， 张建文1,2   

  1. 1. 北京化工大学机电工程学院，北京 100029 2. 北京化工大学安全工程交叉学科研究中心，北京 100029
  • 收稿日期:2023-07-21 修回日期:2023-10-02 出版日期:2024-03-28 发布日期:2024-03-27
  • 通讯作者: 王倩琳 qianlinwangbuct@gmail.com
  • 基金资助:
    国家自然科学基金项目;国家重点研发计划项目

Deep mining of risk weaknesses for petrochemical processes based on alarm logs

Yingqi ZHU1,2,  Qianlin WANG1,2*,  Dongsheng ZHANG1,2,  Zhan DOU1,2,  Jianwen ZHANG1,2   

  1. 1. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China 2. Interdisciplinary Research Center for Chemical Process Safety, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2023-07-21 Revised:2023-10-02 Online:2024-03-28 Published:2024-03-27

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摘要： 在复杂炼化生产过程中，工艺报警日志蕴含着丰富的潜在风险信息，有助于揭示危险性根源、预防过程安全事故发生。为此，本工作提出了一种基于报警日志的炼化过程风险薄弱点深度挖掘方法。首先，针对本工作类型的工艺报警日志，利用Word2Vec词嵌入技术进行向量化预处理，同时通过Pearson相关系数法解析日志间的关联关系，以获取相关系数矩阵。其次，引入复杂网络(CN)理论，将相关系数矩阵转化为布尔矩阵，构建复杂炼化过程的风险表征网络模型。再次，采用逼近理想解排序法(TOPSIS)对所构建的网络模型开展节点重要度精确评估，主要涵盖度中心性、接近中心性和特征向量中心性3个指标。最后，根据网络节点重要度排序的优先级，可深度挖掘复杂炼化过程的风险薄弱点。以某套柴油加氢装置为例，分析结果表明，该方法可准确、有效地提取报警等级为“高高报(HH)”或“高报(HI)”的工艺报警日志，且与复杂炼化过程的实际运行工况相符。

关键词: 风险薄弱点, 报警日志, 节点重要度, 复杂网络（CN）, 逼近理想解排序法（TOPSIS）

Abstract: There are highly dangerous factors in the complex petrochemical processes. The raw materials and products have the characteristics of flammable, explosive, toxic, or harmful. The major dangers would be easily caused by a slight carelessness. During the complex petrochemical processes, a great number of potential risk information is contained in the process alarm logs, which is conductive to reveal the root cause of danger incidents and prevent the occurrence of safety accidents. It is important to make full use of alarm logs for the complex petrochemical processes. Therefore, a deep mining method of risk weaknesses for petrochemical processes is proposed based on alarm logs in this work. Firstly, a word embedded technology-Word2Vec is introduced to pre-process the text-type alarm logs and make them to vectorial data, so the text-type alarm logs are converted and quantized. The Pearson correlation coefficient is further applied to analyze the relationship between these alarm logs and obtain the correlation matrix. Secondly, according to the theory of complex networks (CN), the correlation matrix should be transformed into a Boolean matrix, and then the risk character network could be established for complex petrochemical processes. Thirdly, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to accurately assess the node importance of the established network model. This work involves three indicators: degree centrality, proximity centrality, and eigenvector centrality. Finally, the risk weaknesses of petrochemical processes can be deeply mined based on the priority of network node importance. A diesel hydrotreating unit is selected as the test case. Results show that the proposed method can accurately and effectively mine the process alarm logs with the alarm levels of "High High (HH)" and "High (HI)", which is consistent with the actual operating conditions of petrochemical processes.

Key words: Risk Weaknesses, Alarm Logs, Node Importance, Complex Networks (CN), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)