Dynamic risk assessment of hybrid hydrogen-gasoline fueling stations using complex network analysis and time-series data

被引:5
|
作者
Kang, Jian [1 ]
Wang, Zhixing [1 ]
Jin, Hao [2 ]
Dai, Haoyuan [1 ]
Zhang, Jixin [1 ]
Wang, Lidan [1 ]
机构
[1] Beijing Inst Petrochem Technol, Dept Safety Engn, Beijing 102617, Peoples R China
[2] CSSC, Res Inst 714, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
station; Complex networks; Time series risk assessment; Hybrid hydrogen-gasoline fueling; (TOWA)-(TOWGA) hybrid operator; FIRE;
D O I
10.1016/j.ijhydene.2023.04.212
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Since hybrid hydrogen-gasoline fueling stations store two types of hazardous chemicals, the number of victims might be substantially higher than at gas or hydrogen fueling stations during a leakage or explosion. Therefore, it is crucial to conduct an in-depth analysis and risk assessment of hybrid hydrogen-gasoline fueling stations. We establish a time series risk assessment model and use complex network analysis to analyze potential fire and explosion events in hybrid hydrogen-gasoline fueling stations. The complex network model is used to assess the structural characteristics of the complex hybrid hydrogengasoline fueling stations, extract the accident causal chain, and explain the relationship between the accident causal factors and the system's risk from a multi-dimensional perspective. Subsequently, time-ordered weighted averaging (TOWA) and time-ordered weighted geometric averaging (TOWGA) operators are incorporated into the complex network model. The TOWA-TOWGA hybrid operator combines the evaluation values of the summer and winter periods to obtain the dynamic risk assessment results. The static and dynamic assessment results are used to determine the degree of influence of the accident causal factors on the system risk in different periods and dimensions. The information is suitable for developing highly targeted measures to prevent/control high-risk disaster events in hybrid hydrogen-gasoline fueling stations. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:30608 / 30619
页数:12
相关论文
共 50 条
  • [21] Time-series Anonymization of Tabular Health Data using Generative Adversarial Network
    Hashemi, Atiye Sadat
    Etminani, Kobra
    Soliman, Amira
    Hamed, Omar
    Lundstrom, Jens
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [22] An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network
    Arepalli, Peda Gopi
    Khetavath, Jairam Naik
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (60) : 125275 - 125294
  • [23] An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network
    Peda Gopi Arepalli
    Jairam Naik Khetavath
    Environmental Science and Pollution Research, 2023, 30 : 125275 - 125294
  • [26] Using Time-Series MODIS Data for Agricultural Drought Analysis in Texas
    Peng, Chunming
    Di, Liping
    Deng, Meixia
    Yagci, Ali
    2012 FIRST INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS), 2012, : 168 - 173
  • [27] ANALYSIS OF RESIDENTIAL DEMAND FOR WATER USING MICRO TIME-SERIES DATA
    DANIELSON, LE
    WATER RESOURCES RESEARCH, 1979, 15 (04) : 763 - 767
  • [28] ANALYSIS OF EXTUBATION READINESS USING PHYSIOLOGIC TIME-SERIES DATA IN A CTICU
    Friedland, Sarah
    Mueller, Dana
    Alfaro, Edgar
    Merson, Claire
    Coufal, Nicole
    Rao, Rohit
    CRITICAL CARE MEDICINE, 2024, 52
  • [29] Landslide data analysis using various time-series forecasting models
    Aggarwal, Akarsh
    Alshehri, Mohammed
    Kumar, Manoj
    Alfarraj, Osama
    Sharma, Purushottam
    Pardasani, Kamal Raj
    COMPUTERS & ELECTRICAL ENGINEERING, 2020, 88
  • [30] Dynamic Time Warping for Quantitative Analysis of Tracer Study Time-Series Water Quality Data
    Woo, Hyoungmin
    Boccelli, Dominic L.
    Uber, James G.
    Janke, Robert
    Su, Yuan
    JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2019, 145 (12)