IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v293y2024ics0360544224004614.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this article

Modeling risk characterization networks for chemical processes based on multi-variate data

Author

Listed:
  • Wang, Qianlin
  • Han, Jiaqi
  • Chen, Feng
  • Hu, Su
  • Yun, Cheng
  • Dou, Zhan
  • Yan, Tingjun
  • Yang, Guoan

Abstract

Risk characterization plays a crucial role in ensuring safe and stable chemical processes. However, existing studies cannot mine the deep-level relationships between multi-variate process data, resulting in un-detailed characterization results. Hence a multi-variate data-driven modeling method of risk characterization networks is proposed for chemical processes in this paper. The method particularly combines the Spearman's rank correlation coefficient and Apriori algorithm. Firstly, the Spearman's rank correlation coefficient is used to analyze the correlations between multi-variate process data. Secondly, the Apriori algorithm is further applied to mine the internal association rules of process data. Thirdly, a risk characterization network model is established using the complex network theory. To illustrate its validity, the Tennessee Eastman Process (TEP) and a catalytic cracking process are selected as test cases. Results show that the risk states of chemical processes can be effectively characterized, and the risk roots can also be reasonably identified and traced.

Suggested Citation

  • Wang, Qianlin & Han, Jiaqi & Chen, Feng & Hu, Su & Yun, Cheng & Dou, Zhan & Yan, Tingjun & Yang, Guoan, 2024. "Modeling risk characterization networks for chemical processes based on multi-variate data," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224004614
    DOI: 10.1016/j.energy.2024.130689
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224004614
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130689?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ren, Yinghua & Zhao, Wanru & You, Wanhai & Zhai, Kaikai, 2021. "Multiscale and partial correlation networks analysis of risk connectedness in global equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Chen, Jianjun & Yang, Siyu & Qian, Yu, 2019. "A novel path for carbon-rich resource utilization with lower emission and higher efficiency: An integrated process of coal gasification and coking to methanol production," Energy, Elsevier, vol. 177(C), pages 304-318.
    3. Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
    4. Zhang, Hongwei & Wang, Ying & Yang, Cai & Guo, Yaoqi, 2021. "The impact of country risk on energy trade patterns based on complex network and panel regression analyses," Energy, Elsevier, vol. 222(C).
    5. Meng, Huixing & Kloul, Leïla & Rauzy, Antoine, 2018. "Modeling patterns for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 111-123.
    6. Zhou, Ying & Li, Chenshuang & Ding, Lieyun & Sekula, Przemyslaw & Love, Peter E.D. & Zhou, Cheng, 2019. "Combining association rules mining with complex networks to monitor coupled risks," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 194-208.
    7. Zhang, Wen-Yao & Wei, Zong-Wen & Wang, Bing-Hong & Han, Xiao-Pu, 2016. "Measuring mixing patterns in complex networks by Spearman rank correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 440-450.
    8. Kaneko, Fujio & Yuzui, Tomohiro, 2023. "Novel method of dynamic event tree keeping the number of simulations in risk analysis small," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
    10. Sheikh, Muhammad Sameer & Regan, Amelia, 2022. "A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    11. Kiss, Anton A. & Smith, Robin, 2020. "Rethinking energy use in distillation processes for a more sustainable chemical industry," Energy, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

    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. Fu, Lipeng & Wang, Xueqing & Zhao, Heng & Li, Mengnan, 2022. "Interactions among safety risks in metro deep foundation pit projects: An association rule mining-based modeling framework," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    3. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    4. Wiranarongkorn, Kunlanan & Im-orb, Karittha & Panpranot, Joongjai & Maréchal, François & Arpornwichanop, Amornchai, 2021. "Exergy and exergoeconomic analyses of sustainable furfural production via reactive distillation," Energy, Elsevier, vol. 226(C).
    5. Marina, A. & Spoelstra, S. & Zondag, H.A. & Wemmers, A.K., 2021. "An estimation of the European industrial heat pump market potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    6. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    7. Letitia Petrescu & Codruta-Maria Cormos, 2022. "Classical and Process Intensification Methods for Acetic Acid Concentration: Technical and Environmental Assessment," Energies, MDPI, vol. 15(21), pages 1-23, October.
    8. Ming Fang & Yi Zhang & Mengjue Zhu & Shaopei Chen, 2022. "Cause Mechanism of Metro Collapse Accident Based on Risk Coupling," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    9. Siddig S. Khalafalla & Umer Zahid & Abdul Gani Abdul Jameel & Usama Ahmed & Feraih S. Alenazey & Chul-Jin Lee, 2020. "Conceptual Design Development of Coal-to-Methanol Process with Carbon Capture and Utilization," Energies, MDPI, vol. 13(23), pages 1-21, December.
    10. Albuquerque, Allan Almeida & Ng, Flora T.T. & Danielski, Leandro & Stragevitch, Luiz, 2022. "Reactive separation processes applied to biodiesel production from residual oils and fats: Design, optimization and techno-economic assessment of routes using solid catalysts," Energy, Elsevier, vol. 240(C).
    11. Li, Yingli & Huang, Jianbai & Zhang, Hongwei, 2022. "The impact of country risks on cobalt trade patterns from the perspective of the industrial chain," Resources Policy, Elsevier, vol. 77(C).
    12. Gaogeng Zhu & Guoming Chen & Jingyu Zhu & Xiangkun Meng & Xinhong Li, 2022. "Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry," IJERPH, MDPI, vol. 19(12), pages 1-27, June.
    13. Guedes do Nascimento, Leomário & Costa Monteiro, Luciane Pimentel & de Cássia Colman Simões, Rita & Prata, Diego Martinez, 2023. "Eco-efficiency analysis and intensification of the biodiesel production process through vapor recompression strategy," Energy, Elsevier, vol. 275(C).
    14. Hou, Rui & Zhang, Nachuan & Yang, Chengsheng & Zhao, Jing & Li, Peng & Sun, Bo, 2023. "A novel structure of natural gas, electricity, and methanol production using a combined reforming cycle: Integration of biogas upgrading, liquefied natural gas re-gasification, power plant, and methan," Energy, Elsevier, vol. 270(C).
    15. Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).
    16. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    17. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    19. Ma, Qian & Chang, Yuan & Yuan, Bo & Song, Zhaozheng & Xue, Jinjun & Jiang, Qingzhe, 2022. "Utilizing carbon dioxide from refinery flue gas for methanol production: System design and assessment," Energy, Elsevier, vol. 249(C).
    20. Feng, Chao & Liu, Yu-Qi & Yang, Jun, 2024. "Do energy trade patterns affect renewable energy development? The threshold role of digital economy and economic freedom," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

    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:eee:energy:v:293:y:2024:i:c:s0360544224004614. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.