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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    4. Kiss, Anton A. & Smith, Robin, 2020. "Rethinking energy use in distillation processes for a more sustainable chemical industry," Energy, Elsevier, vol. 203(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. 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.
    7. 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).
    8. 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).
    9. 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.
    10. 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).
    11. 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).
    Full references (including those not matched with items on IDEAS)

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