IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p726-d728512.html
   My bibliography  Save this article

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Author

Listed:
  • Haotian Li

    (School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Kun Huang

    (School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Qin Zeng

    (PetroChina Southwest Oil and Gas Field Gas Branch, Chengdu 610500, China)

  • Chong Sun

    (Sinopec Petroleum Engineering Zhongyuan Corporation, Puyang 457000, China)

Abstract

Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized.

Suggested Citation

  • Haotian Li & Kun Huang & Qin Zeng & Chong Sun, 2022. "Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review," Energies, MDPI, vol. 15(3), pages 1-30, January.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:726-:d:728512
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/726/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/3/726/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:726-:d:728512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.