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A novel assessment method for residual strength of CO2 pipelines with multiple defects based on RF-MLP

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  • Li, Yan
  • Chen, Zhanfeng
  • Wang, Wen
  • Han, Ke
  • Shuai, Yi
  • Wang, Ganxun

Abstract

CO2 pipelines play a crucial role in Carbon Capture, Utilization, and Storage (CCUS), making the accurate prediction of the residual strength of pipelines with multiple corrosion defects essential for assessing reliability and remaining service life. This study proposes an RF-MLP method, where Random Forest (RF) is employed for feature analysis to identify the most influential factors affecting corrosion defects, and Multi-Layer Perceptron (MLP) is used for predicting the effective depth of corrosion defects. A finite element model was established to generate a dataset for training and validation. Through comparison with Support Vector Machine (SVM), MLP and Kriging model was ultimately selected as the optimal prediction model due to its superior performance. The RF-MLP approach was validated against experimental data, demonstrating high accuracy in predicting both the effective depth of defects and the residual strength of pipelines. This method provides an innovative and reliable approach to assessing the structural integrity of pipelines with corrosion defects in CCUS systems.

Suggested Citation

  • Li, Yan & Chen, Zhanfeng & Wang, Wen & Han, Ke & Shuai, Yi & Wang, Ganxun, 2025. "A novel assessment method for residual strength of CO2 pipelines with multiple defects based on RF-MLP," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025002893
    DOI: 10.1016/j.ress.2025.111088
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    References listed on IDEAS

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    1. Rafew, S. M. & Kabir, Golam, 2026. "Application of interactive threat matrix induced system dynamics model to determine risk probability and resilient policy measures for CO2 pipelines," Reliability Engineering and System Safety, Elsevier, vol. 269(C).
    2. Miao, Xingyuan & Ma, Yinghan & Sun, Xianglong & Zhao, Hong, 2025. "Residual strength prediction of hydrogen-blended natural gas pipelines based on incremental knowledge distillation," Energy, Elsevier, vol. 341(C).

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