引用本文:夏晓健,杨国威,林德源,李清,朱仁政,杨小佳,陈奕扬,万芯瑗,严康骅,韩纪层,陈云翔,洪毅成,陈天鹏.基于腐蚀大数据的碳钢海洋大气腐蚀行为[J].中国表面工程,2024,37(2):58~71
XIA Xiaojian,YANG Guowei,LIN Deyuan,LI Qing,ZHU Renzheng,YANG Xiaojia,CHEN Yiyang,WAN Xinyuan,YAN Kanghua,HAN Jiceng,CHEN Yunxiang,HONG Yicheng,CHEN Tianpeng.Marine Atmospheric Corrosion Behavior of Carbon Steel Based on Corrosion Big Data[J].China Surface Engineering,2024,37(2):58~71
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基于腐蚀大数据的碳钢海洋大气腐蚀行为
夏晓健1, 杨国威2, 林德源1, 李清2, 朱仁政2, 杨小佳2, 陈奕扬1, 万芯瑗1, 严康骅1, 韩纪层1, 陈云翔1, 洪毅成1, 陈天鹏3
1.国网福建省电力有限公司电力科学研究院 福州 350007;2.北京科技大学新材料技术研究院 北京 100083;3.国网福建省电力有限公司莆田供电公司 莆田 351100
摘要:
随着“一带一路”和“海洋强国”等重大战略的深入推进,沿海地区及海上重大装备设施面临着空前严峻的海洋环境挑战。深入研究海洋大气腐蚀行为,对于延缓腐蚀过程、确保设施安全运行以及防止重大安全事故和经济损失具有至关重要的意义。以 Q235 碳钢作为研究对象,基于腐蚀大数据技术,通过周期浸泡加速试验获取实时连续的腐蚀数据,研究环境因子 Cl? 、HSO3 ? 及 pH 值对碳钢海洋大气腐蚀行为的影响。结果表明:随着 Cl? 、HSO3 ? 浓度的增加或 pH 值的降低,腐蚀累积量呈不断上升的趋势,腐蚀大数据采集结果与碳钢试样腐蚀形貌及锈层分析结果一致。随试验的进行,当 Cl? 浓度较低时, 锈层朝着更加稳定的方向发展;Cl? 浓度较高时,锈层稳定性不断下降。0.05% HSO3 ? 浓度下,腐蚀始终以相对较低的速率匀速进行;浓度为 0.1%时,腐蚀先是匀速进行,但由于腐蚀后期形成了具有一定保护性的锈层,腐蚀速率有所下降;当浓度达到 0.2%时,腐蚀速率在试验后期反而加快,锈层难以维持相对稳定的状态。试验环境 pH=5 或 3 时,整个试验周期的腐蚀速率整体呈下降趋势;pH=1 时,腐蚀速率呈持续上升趋势。腐蚀大数据方法与传统挂片法研究结果具有较高的一致性,验证了腐蚀大数据手段对于环境腐蚀研究的可靠性,研究结果可为后续开展数据分析与挖掘工作奠定基础。
关键词:  海洋大气腐蚀  碳钢  腐蚀传感器  腐蚀大数据
DOI:10.11933/j.issn.1007-9289.20230710002
分类号:TG172
基金项目:国网福建省电力有限公司科技项目(52130422000T)
Marine Atmospheric Corrosion Behavior of Carbon Steel Based on Corrosion Big Data
XIA Xiaojian1, YANG Guowei2, LIN Deyuan1, LI Qing2, ZHU Renzheng2, YANG Xiaojia2, CHEN Yiyang1, WAN Xinyuan1, YAN Kanghua1, HAN Jiceng1, CHEN Yunxiang1, HONG Yicheng1, CHEN Tianpeng3
1.Electric Power Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350007 , China;2.Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083 , China;3.Putian Power Supply Company, State Gird Fujian Electric Power Co., Ltd., Putian 351100 , China
Abstract:
The “Belt and Road” initiative and the aspiration to evolve into a “Maritime Powerhouse” have highlighted the challenges of marine atmospheric corrosion affecting coastal regions and key maritime infrastructure. This phenomenon presents a critical challenge for the global maritime sector, emphasizing the need for in-depth understanding and effective mitigation strategies to preserve the integrity and operational safety of maritime facilities, thereby preventing significant safety incidents and economic losses. This study focuses on Q235 carbon steel, a material extensively used in maritime constructions, by applying advanced corrosion big data technology. The research methodology incorporates cyclic immersion acceleration tests, enabling the collection of continuous real-time corrosion data. This approach is vital for a comprehensive analysis of the effects of environmental factors such as Cl ? , HSO3 ? , and pH levels on the corrosion behavior of carbon steel in marine atmospheres. The findings of this study indicate a clear correlation between increased concentrations of Cl? and HSO3 ? or a decrease in pH levels and the acceleration of the corrosion process. The gathered data aligns with the observed physical corrosion morphology and rust layer analysis of the steel samples, demonstrating the robustness and reliability of the data-driven approach. This study highlights that at lower Cl? concentrations, the rust layer tends toward greater stability whereas higher concentrations result in decreased stability. With different HSO3 ? concentrations, the corrosion behavior varies: at 0.05%, corrosion proceeds at a steady low rate; at 0.1%, a protective rust layer forms, slowing the corrosion rate; and at 0.2%, the rate increases in the later stages, challenging the stability of the rust layer. In environments with pH values of 5 or 3, the overall trend is a decline in the corrosion rates, in contrast to a pH of 1, where the rate consistently increases. A significant aspect of this study is the integration of traditional corrosion research methodologies with modern big data analytics. This innovative approach represents a substantial advancement in corrosion research, combining the proven reliability of traditional methods with the extensive analytical capabilities of modern data science. The consistency of the big data findings with traditional coupon methods validates this approach, highlighting its effectiveness in providing deep and comprehensive insights into environmental corrosion processes. Furthermore, this research utilizes a range of advanced experimental techniques, such as scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), confocal laser scanning microscopy, and various electrochemical tests. These methods have been instrumental in characterizing the morphological and chemical properties of the rust layer, thereby enriching the overall findings of this study. This extensive study provides a detailed examination of marine atmospheric corrosion, contributing significantly to the field by offering new perspectives and robust methodologies. These contributions are crucial for effectively assessing and mitigating corrosion in maritime environments, aligning with international maritime strategies and infrastructure safety objectives. In summary, this study marks a paradigm shift in corrosion research, blending traditional experimental methods with the advanced analytical capabilities of big data. This integration opens new avenues for future investigations and innovations in the field, underscoring the importance of data-driven approaches in understanding and addressing complex environmental challenges. With its comprehensive analysis, innovative methodology, and significant findings, this research not only deepens the understanding of marine atmospheric corrosion but also establishes a solid foundation for future data-driven studies and solutions in maritime engineering and environmental protection. This study is a testament to the power of integrating traditional research methods with modern data analytics to address complex environmental issues, paving the way for further advancements in the field. Additionally, this study underscores the significance of the ongoing technological advancements in corrosion research. As environmental conditions continue to evolve, adapting and refining research methodologies to keep pace with them is becoming increasingly important. The use of big data and advanced analytical techniques in this study not only demonstrates a progressive approach to understanding marine atmospheric corrosion but also serves as a model for future studies in similar fields. This approach highlights the necessity of continuous innovation and adaptation in scientific research, particularly in areas with significant practical implications such as maritime infrastructure and environmental protection. Embracing these innovative methodologies ensures that research remains relevant, effective, and capable of addressing the complex challenges posed by a dynamically changing environment.
Key words:  marine atmospheric corrosion  carbon steel  corrosion sensor  corrosion big data
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