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Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State

Author

Listed:
  • Juhyun Kim

    (Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Sunlee Han

    (Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Daehee Kim

    (Korea CCUS Association, Sejong 30103, Republic of Korea)

  • Youngsoo Lee

    (Department of Mineral Resource and Energy Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
    Department of Environment and Energy, Jeonbuk National University, Jeonju 54896, Republic of Korea)

Abstract

This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline’s inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model’s accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model’s classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions.

Suggested Citation

  • Juhyun Kim & Sunlee Han & Daehee Kim & Youngsoo Lee, 2024. "Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State," Energies, MDPI, vol. 17(21), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5517-:d:1513904
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    References listed on IDEAS

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    1. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
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