Interpreting microbiologically influenced stress corrosion with machine learning and theoretical analysis

• Microstructure has the greatest influence on the initial stage of microbial corrosion. • Stress has a full cycle influence on microbial corrosion. • Biological nitrate reduction and stress synergistically accelerate stress corrosion. Numerous tests have demonstrated the impact of microbiologically influenced stress corrosion (MISC) on oil and gas pipelines, but the dynamic corrosion process and its influencing variables remain unclear. In this paper, through material and environment data collection, we analyzed the important factors to MISC based on the random forest model, which were quantity of bacteria, kernel average misorientation and prior austenite grain boundary of the material. Based on this, theoretical explanation of stress and nitrate-reducing bacteria promoting stress corrosion cracking was provided. Results of this study will serve to gain further knowledge of MISC and guide future protection efforts.

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Publication Details

Journal
Corrosion Communications
Published
2024-11-27
DOI
https://doi.org/10.1016/j.corcom.2024.01.004
Citations
4
Primary Topic
Corrosion Behavior and Inhibition
Type
article
Field-Weighted Citation Impact
0.47
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article

Interpreting microbiologically influenced stress corrosion with machine learning and theoretical analysis

Cuiwei Du, Boxin Wei, Bo Liu, Xiaogang Li
4 citations
Corrosion Communications
Corrosion Behavior and Inhibition
0.47
article

Interpreting microbiologically influenced stress corrosion with machine learning and theoretical analysis

Cuiwei Du, Boxin Wei, Bo Liu, Xiaogang Li
article en
4 citations

Abstract

• Microstructure has the greatest influence on the initial stage of microbial corrosion. • Stress has a full cycle influence on microbial corrosion. • Biological nitrate reduction and stress synergistically accelerate stress corrosion. Numerous tests have demonstrated the impact of microbiologically influenced stress corrosion (MISC) on oil and gas pipelines, but the dynamic corrosion process and its influencing variables remain unclear. In this paper, through material and environment data collection, we analyzed the important factors to MISC based on the random forest model, which were quantity of bacteria, kernel average misorientation and prior austenite grain boundary of the material. Based on this, theoretical explanation of stress and nitrate-reducing bacteria promoting stress corrosion cracking was provided. Results of this study will serve to gain further knowledge of MISC and guide future protection efforts.

Corrosion CommunicationsVol. 18
Xi'an Shiyou University (CN), Chinese Academy of Sciences (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 37%
Corrosion Behavior and Inhibition
0.47
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