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An online real-time estimation tool of leakage parameters for hazardous liquid pipelines

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  • Zheng, Jianqin
  • Dai, Yuanhao
  • Liang, Yongtu
  • Liao, Qi
  • Zhang, Haoran

Abstract

Hazardous liquid pipeline (HLP) leaks not only result in energy waste and environmental pollution, but also pose a threat to people's lives and property. The estimation of leakage parameters is an essential part of risk assessment and environment pollution assessment. However, current common leak detection methods are mainly based on physical models with assumptions and are susceptible to noise. Limited historical leakage data render it impossible to develop a leak model in advance. To address this problem, this study establishes a pipeline digital twin model that simulates a pipeline leak to generate leakage data. A conditional variational auto-encoder (CVAE) framework is proposed to estimate the leakage parameters based on data detected by upstream and downstream meters once the HLP leak occurs. CVAE can treat the high-dimensional detected data as labels to overcome the dimensionality problem. Based on the CVAE framework, an online real-time leakage parameter estimation tool for HLP is formed. To qualify the performance of the approach, a sensitivity analysis for the structure of the CVAE framework is evaluated. Finally, four examples demonstrate the effectiveness, stability, and applicability of the proposed method.

Suggested Citation

  • Zheng, Jianqin & Dai, Yuanhao & Liang, Yongtu & Liao, Qi & Zhang, Haoran, 2020. "An online real-time estimation tool of leakage parameters for hazardous liquid pipelines," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:ijocip:v:31:y:2020:i:c:s1874548220300536
    DOI: 10.1016/j.ijcip.2020.100389
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    References listed on IDEAS

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    1. Li, Zhengbing & Feng, Huixia & Liang, Yongtu & Xu, Ning & Nie, Siming & Zhang, Haoran, 2019. "A leakage risk assessment method for hazardous liquid pipeline based on Markov chain Monte Carlo," International Journal of Critical Infrastructure Protection, Elsevier, vol. 27(C).
    2. Zhang, Haoran & Liang, Yongtu & Liao, Qi & Wu, Mengyu & Yan, Xiaohan, 2017. "A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations," Energy, Elsevier, vol. 119(C), pages 612-628.
    3. B. Zhao, 2014. "Qingdao pipeline explosion: introductions and reflections," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 1299-1305, November.
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    Cited by:

    1. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    3. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Lu, Xinyi & Tu, Renfu & Liao, Qi & Xu, Ning & Xia, Yuheng, 2023. "A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction," Energy, Elsevier, vol. 263(PD).
    4. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
    5. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).

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