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A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines

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  • Li, Xinhong
  • Jia, Ruichao
  • Zhang, Renren
  • Yang, Shangyu
  • Chen, Guoming

Abstract

Corrosion is an important reason for the structural degradation of offshore oil pipelines, which may cause serious economic loss and environmental pollution. Nowadays the digitalized devices make a number of monitoring data become available. The prediction of corrosion degradation based on monitoring data becomes an efficient tool to prevent corrosion failure of offshore oil pipelines. This paper integrates KPCA and BRANN techniques to develop a novel data-driven model for corrosion degradation prediction of offshore oil pipelines. The model can eliminate the redundant information from the original monitoring data and improve the robustness by regularization constraints. KPCA is applied to reduce the dimension of the factors affecting pipeline corrosion, and the extracted principal components of corrosion variables are inputted in BRANN to build a corrosion degradation prediction model. The data with dimension reduction are divided into training set and validation set. The model is compared with BRANN alone and KPCA-LMANN model, which indicates KPCA-BRANN model presents superiority in the robustness and prediction accuracy (MSEÂ =Â 0.46%; R2=0.99). The proposed model can be used as an online prediction module of digitized process safety system, and support the reliability assessment and maintenance planning of corroded subsea pipelines.

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  • Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007092
    DOI: 10.1016/j.ress.2021.108231
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    2. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    3. Jiang, Shengyu & He, Rui & Chen, Guoming & Zhu, Yuan & Shi, Jiaming & Liu, Kang & Chang, Yuanjiang, 2023. "Semi-supervised health assessment of pipeline systems based on optical fiber monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. 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).
    5. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Jose E. Naranjo & Gustavo Caiza & Rommel Velastegui & Maritza Castro & Andrea Alarcon-Ortiz & Marcelo V. Garcia, 2022. "A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0," Sustainability, MDPI, vol. 14(24), pages 1-22, December.

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