IDEAS home Printed from https://ideas.repec.org/a/hin/complx/6250483.html
   My bibliography  Save this article

Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park

Author

Listed:
  • Yaguang Kong
  • Chenfeng Xie
  • Song Zheng
  • Peng Jiang
  • Meng Guan
  • Fang Wang

Abstract

The production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk values of MHIs and improve accident prevention ability of CIP, we need a method that can combine dynamic prediction and assessment. Quantitative risk assessment (QRA) is not capable of modelling risk variations during the operation of a process. Therefore, this paper adopts the data-driven approach. Inspired by visual qualitative analysis and quantitative analysis, a dynamic early warning method is proposed for MHIs. We can get the future trend of these key parameters by using strongly correlation variables to predict key parameters. Fuzzy evaluation analysis is performed on the risk levels of key parameters, and the dynamic evaluation index of these MHIs is obtained. This method can be applied to the dynamic evaluation of MHIs system in CIP. It can contribute to the safety of CIP in some aspects.

Suggested Citation

  • Yaguang Kong & Chenfeng Xie & Song Zheng & Peng Jiang & Meng Guan & Fang Wang, 2019. "Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park," Complexity, Hindawi, vol. 2019, pages 1-18, May.
    • Handle: RePEc:hin:complx:6250483
    DOI: 10.1155/2019/6250483
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/6250483.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/6250483.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/6250483?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2012. "Dynamic risk analysis using bow-tie approach," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 36-44.
    3. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
    4. Bai, Yanping & Zhang, Haixia & Hao, Yilong, 2009. "The performance of the backpropagation algorithm with varying slope of the activation function," Chaos, Solitons & Fractals, Elsevier, vol. 40(1), pages 69-77.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ge He & Li Zhou & Yiyang Dai & Yagu Dang & Xu Ji, 2020. "Coal Industrial Supply Chain Network and Associated Evaluation Models," Sustainability, MDPI, vol. 12(23), pages 1-20, November.
    2. Tao Zeng & Guohua Chen & Yunfeng Yang & Genserik Reniers & Yixin Zhao & Xia Liu, 2020. "A Systematic Literature Review on Safety Research Related to Chemical Industrial Parks," Sustainability, MDPI, vol. 12(14), pages 1-27, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
    2. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    3. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    4. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    5. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    6. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Casualty analysis methodology and taxonomy for FPSO accident analysis," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    8. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    9. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    10. Jing Bai & Jiahui Wang & Jin Ran & Xingyuan Li & Chuang Tu, 2024. "An Improved Neural Network Algorithm for Energy Consumption Forecasting," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    11. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    12. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Ma, Teng & Li, Ming-Jia & Xu, Jin-Liang & Cao, Feng, 2019. "Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant," Energy, Elsevier, vol. 175(C), pages 123-138.
    14. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    15. Landucci, Gabriele & Argenti, Francesca & Tugnoli, Alessandro & Cozzani, Valerio, 2015. "Quantitative assessment of safety barrier performance in the prevention of domino scenarios triggered by fire," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 30-43.
    16. Minglu Ma & Min Su & Shuyu Li & Feng Jiang & Rongrong Li, 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    17. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
    18. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
    19. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    20. Yang Changwei & Li Zonghao & Guo Xueyan & Yu Wenying & Jin Jing & Zhu Liang, 2019. "Application of BP Neural Network Model in Risk Evaluation of Railway Construction," Complexity, Hindawi, vol. 2019, pages 1-12, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:6250483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.