收藏切换
Early warning method for abnormal states in petrochemical equipment based on probability distribution functions
收藏切换
PDF
Shengnan WU1, 2, Yiming HU1, 2, Laibin ZHANG1, 2, Xueqi WANG1, 2, 3, Ruibo WANG3
China Safety Science Journal | 2024, 34(7) : 113 - 122
Less
收藏切换
China Safety Science Journal | 2024, 34(7): 113-122
Safety engineering technology
Early warning method for abnormal states in petrochemical equipment based on probability distribution functions
Full
Shengnan WU1, 2, Yiming HU1, 2, Laibin ZHANG1, 2, Xueqi WANG1, 2, 3, Ruibo WANG3
Affiliations
  • 1 College of Safety and Ocean Engineering,China University of Petroleum (Beijing),Beijing 102249,China
  • 2 Key Laboratory of Oil and Gas Safety and Emergency Technology,Ministry of Emergency Management,Beijing 102249,China
  • 3 Research Institute of Safety and Environment Technology,China National Petroleum Corporation,Dalian Liaoning 116000,China
Published: 2024-07-28 doi: 10.16265/j.cnki.issn1003-3033.2024.07.0256
Outline
收藏切换

To mitigate the risks of leakage,fires and explosions in petrochemical equipment,focusing on a typical catalytic cracking unit,a novel early warning method for detecting abnormal states using probability distribution functions was introduced. Spline fitting principles were used to uncover the trends in operating parameters such as pressure,temperature and flow rate over time,and to extract characteristic parameters such as deviation rate and deviation amount. By employing the Weibull distribution,the failure probability distribution function of the equipment was determined. The extracted characteristic parameters were integrated with the failure function to construct a probabilistic distribution mathematical model incorporating these features. Based on this model,a comprehensive early warning process was developed,facilitating real-time risk assessment and anomaly detection during the catalytic cracking process. The findings demonstrate that this method can effectively predict anomalies under conditions of oscillation,step changes,and gradual trends in operating parameters. Compared to traditional instrument systems,this early warning method advances the warning time by 87 to 621 seconds,addressing the limitation of limited response time following single-threshold alarms in the conventional systems. Furthermore,a comparison of various data processing methods reveals that the early warning model based on spline fitting exhibits superior performance.

probability distribution function  /  petrochemical equipment  /  abnormal states  /  early warning  /  operating parameters
Shengnan WU, Yiming HU, Laibin ZHANG, Xueqi WANG, Ruibo WANG. Early warning method for abnormal states in petrochemical equipment based on probability distribution functions[J]. China Safety Science Journal, 2024 , 34 (7) : 113 -122 . DOI: 10.16265/j.cnki.issn1003-3033.2024.07.0256
Year 2024 volume 34 Issue 7
PDF
382
168
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.07.0256
  • Receive Date:2024-01-07
  • Online Date:2025-07-09
  • Published:2024-07-28
Article Data
Affiliations
History
  • Received:2024-01-07
  • Revised:2024-04-23
Funding
Affiliations
    1 College of Safety and Ocean Engineering,China University of Petroleum (Beijing),Beijing 102249,China
    2 Key Laboratory of Oil and Gas Safety and Emergency Technology,Ministry of Emergency Management,Beijing 102249,China
    3 Research Institute of Safety and Environment Technology,China National Petroleum Corporation,Dalian Liaoning 116000,China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2024.07.0256
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT