Interpreting microbiologically influenced stress corrosion with machine learning and theoretical analysis

被引:0
|
作者
Liu, Bo [1 ]
Wei, Boxin [2 ]
Du, Cuiwei [3 ,4 ]
Li, Xiaogang [3 ,4 ]
机构
[1] Xian Shiyou Univ, Sch Mat Sci & Engn, Xian 710065, Peoples R China
[2] Chinese Acad Sci, Inst Met Res, Shenyang 110016, Peoples R China
[3] Univ Sci & Technol Beijing, Inst Adv Mat & Technol, Beijing 100083, Peoples R China
[4] Natl Mat Corros & Protect Sci Data Ctr, Beijing 100083, Peoples R China
来源
CORROSION COMMUNICATIONS | 2025年 / 18卷
关键词
Microbiologically influenced corrosion; Stress corrosion; Machine learning; Sensitivity analyses; Nitrate-reducing bacteria; AUSTENITIC STAINLESS-STEEL; ANODIC-DISSOLUTION; MICROSTRUCTURE; BEHAVIOR; CRACKING;
D O I
10.1016/j.corcom.2024.01.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Institute of Metal Research, Chinese Academy of Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页码:19 / 27
页数:9
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