金属材料腐蚀预测模型研究进展

doi:10.11902/1005.4537.2022.332

中国腐蚀与防护学报

2023, Vol. 43

Issue (5): 983-991 CSTR: 32134.14.1005.4537.2022.332 DOI: 10.11902/1005.4537.2022.332

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金属材料腐蚀预测模型研究进展

姚勇¹, 刘国军¹, 黎石竹¹, 刘淼然²(

), 陈川², 黄廷城², 林海³, 李展江³, 刘雨薇⁴, 王振尧⁴

^1.广东能源集团科学院技术研究院有限公司广州 510630
^2.中国电器科学研究院股份有限公司广州 510799
^3.湛江海关技术中心湛江 524000
^4.中国科学院金属研究所沈阳 110016

Research Progress on Corrosion Prediction Model of Metallic Materials for Electrical Equipment

YAO Yong¹, LIU Guojun¹, LI Shizhu¹, LIU Miaoran²(

), CHEN Chuan², HUANG Tingcheng², LIN Hai³, LI Zhanjiang³, LIU Yuwei⁴, WANG Zhenyao⁴

^1.Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China
^2.China National Electric Apparatus Research Institute Co., Ltd., Guangzhou 510799, China
^3.Zhanjiang Customs Technology Center, Zhanjiang 524000, China
^4.Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China

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摘要:

对现有腐蚀领域常用腐蚀预测方法，包括函数模型、灰色理论模型、神经网络预测模型，剂量响应函数模型和随机森林模型等进行总结分析，并将现有的腐蚀预测模型分为腐蚀-时间和腐蚀-环境预测模型，介绍了不同腐蚀预测模型的特点和适用范围等。最后，根据电力行业的特点对金属材料的腐蚀预测研究提出了一些展望。

关键词 ：金属材料, 腐蚀, 预测模型, 数据处理, 电力设备

Abstract：

Metallic materials for electrical equipment are affected by many factors related with environment during service, and their corrosion behavior is very complex, therefore, which is difficult to be accurately predicted. The development of computer technology and data analysis technology enriches the prediction methods for corrosion behavior of metallic materials with better accuracy. This paper summarizes and analyzes the existing common corrosion prediction methods in the field of corrosion, including function model, grey theory model, neural network prediction model, dose response function model and random forest model etc., and which then are classified into two types, namely corrosion-time models and corrosion-environment prediction models. Furthermore, the characteristics and application scope of different corrosion prediction models are introduced. Finally, prospects for the corrosion prediction of metallic materials are put forward especially in terms of the demands of power industry.

Key words： metallic material corrosion prediction model data processing electrical equipment

收稿日期: 2022-10-25 32134.14.1005.4537.2022.332

ZTFLH:

TG174.42

基金资助:广东能源集团科学技术研究院有限公司科技项目(STI-PY-21009)

通讯作者: 刘淼然，E-mail: liumr@cei1958.com，研究方向为金属材料大气腐蚀

Corresponding author: LIU Miaoran, E-mail: liumr@cei1958.com

作者简介: 姚勇，男，1981年生，高级工程师

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	刘雨薇
	王振尧
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引用本文:

姚勇, 刘国军, 黎石竹, 刘淼然, 陈川, 黄廷城, 林海, 李展江, 刘雨薇, 王振尧. 金属材料腐蚀预测模型研究进展[J]. 中国腐蚀与防护学报, 2023, 43(5): 983-991.
YAO Yong, LIU Guojun, LI Shizhu, LIU Miaoran, CHEN Chuan, HUANG Tingcheng, LIN Hai, LI Zhanjiang, LIU Yuwei, WANG Zhenyao. Research Progress on Corrosion Prediction Model of Metallic Materials for Electrical Equipment. Journal of Chinese Society for Corrosion and protection, 2023, 43(5): 983-991.

链接本文:

https://www.jcscp.org/CN/10.11902/1005.4537.2022.332 或 https://www.jcscp.org/CN/Y2023/V43/I5/983

图1 不同国家碳钢腐蚀损失随时间变化的动力学曲线[14, 15]

图2 神经网络元示意图

图3 神经网络模型示意图

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