| 引用本文: | 张帆,徐迪,杨小佳,王一品,陈昊,彭非凡,安江峰,程学群,李晓刚.碳钢腐蚀大数据采集传感器在城市大气环境中适应性标定[J].中国表面工程,2024,37(2):1~15 |
| ZHANG Fan,XU Di,YANG Xiaojia,WANG Yipin,CHEN Hao,PENG Feifan,AN Jiangfeng,CHENG Xuequn,LI Xiaogang.Adaptability Calibration of Big Data Acquisition Sensors for Monitoring Carbon Steel Corrosion in Urban Atmospheric Environment[J].China Surface Engineering,2024,37(2):1~15 |
|
| |
|
|
| 本文已被：浏览 745次 下载 720次 |
码上扫一扫！ |
|
|
| 碳钢腐蚀大数据采集传感器在城市大气环境中适应性标定 |
|
张帆1,2,3, 徐迪4, 杨小佳4, 王一品1, 陈昊1, 彭非凡1, 安江峰1,2,3, 程学群4,5, 李晓刚4,5
|
|
1.中国机械总院集团武汉材料保护研究所有限公司 武汉 430030;2.武汉大气淡水环境材料腐蚀国家野外观测科学研究站 武汉 430030;3.新疆尉犁大气环境材料腐蚀国家野外观测科学研究站 尉犁 841500;4.北京科技大学新材料技术研究院 北京 100083;5.北京科技大学北京材料基因组工程先进创新中心 北京 100083
|
|
| 摘要: |
| 传统的腐蚀监测方法和腐蚀评估方法已经无法满足对数据量以及数据连续性的需要。大气腐蚀在线监测技术由于具有数据量大、数据连续及实时性等特点，已被广泛应用。然而，所得数据的准确性和适用性还须通过试验来做进一步验证。采用户外挂片以及电阻传感器和电偶传感器监测 Q235 碳钢在城市大气环境下的腐蚀速率，建立响应面模型，并采用温湿度耦合试验和干湿交替模拟试验进行验证。温湿度耦合试验和干湿交替模拟试验与户外挂片及响应面模型的腐蚀速率变化趋势一致，而温湿度耦合试验得到的腐蚀速率更接近于挂片得到的腐蚀速率。其中，电偶传感器得到的腐蚀速率值更接近于挂片的腐蚀速率值，说明城市大气环境下更适合使用电偶传感器。室内模拟试验中，温度升高会加速薄液膜下阴阳极的电极过程和化学反应，延长反应时间，表面腐蚀产物逐渐致密且均匀，一定程度上可以提高锈层的耐蚀性能。通过碳钢腐蚀传感器在城市大气环境中的适应性标定，可以深入探究大气环境中金属材料的腐蚀机理和过程，并准确评估腐蚀传感器在大气环境中的腐蚀行为，为研究者提供定量描述和分析腐蚀行为的基础数据。 |
| 关键词: 电阻传感器 电偶传感器 响应面模型 温湿度耦合试验 干湿交替模拟试验 |
| DOI：10.11933/j.issn.1007-9289.20230625001 |
| 分类号:TG17 |
| 基金项目:科技基础资源调查项目（2021FY100600） |
|
| Adaptability Calibration of Big Data Acquisition Sensors for Monitoring Carbon Steel Corrosion in Urban Atmospheric Environment |
|
ZHANG Fan1,2,3, XU Di4, YANG Xiaojia4, WANG Yipin1, CHEN Hao1, PENG Feifan1, AN Jiangfeng1,2,3, CHENG Xuequn4,5, LI Xiaogang4,5
|
|
1.China Academy of Machinery Wuhan Research Institute of Materials Protection Co., Ltd.,Wuhan 430030 , China;2.Wuhan Materials Corrosion National Observation and Research Station, Wuhan 430030 , China;3.Yuli Materials Corrosion National Observation and Research Station, Yuli 841500 , China;4.Institute for Advanced Materials and Technology, University of Science and Technology Beijing,Beijing 100083 , China;5.Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing, Beijing 100083 , China
|
| Abstract: |
| Traditional methods for monitoring and evaluating corrosion are affected by the extended experimental period and slow pace; thus, they fail to satisfy the demands for data quantity and data continuity. Techniques for online monitoring of atmospheric corrosion require large amounts of continuous and real-time data, and the obtained big data can be effectively simulated, calculated and modeled using computer software to clarify the metal corrosion process and achieve data sharing. Various techniques for detecting atmospheric corrosion have been widely used. However, the accuracy and validation of the data require further experimental verification. In this study, the corrosion rate of Q235 carbon steel in the urban atmosphere was monitored using an outdoor hanging plate, a resistance sensor, and a galvanic sensor. Subsequently, the response surface model was established, and its validity was confirmed via coupled temperature-humidity experiments and alternate drying-wetting simulation experiments. Confocal laser scanning microscope (CLSM), X-ray diffraction (XRD), scanning electron microscopy (SEM), and electrochemical testing were performed to investigate the effects of outdoor exposure and indoor simulation experiment on the surface rust layer of Q235 carbon steel. The results show that the corrosion rates of the resistance and galvanic sensors are 1.295 and 1.084 times the corrosion rate of the hanging plate, respectively. The variation trends of the corrosion rate of the sensor in the coupled temperature-humidity experiments and alternate drying-wetting simulation experiments are consistent with those of the outdoor hanging and response surface model. In the coupled temperature-humidity experiments, the corrosion rates recorded by the resistance and galvanic sensors are 1.136 and 1.018 times that of the hanging plate, respectively. In the low-temperature low-humidity environment, the corrosion rate of the galvanic sensor is similar to that of the hanging plate method. However, in the high-temperature high-humidity environment, the corrosion rate of the sensor is higher than that of the hanging plate. In the alternate drying-wetting simulation experiment, the corrosion rates of the resistance and galvanic sensors are 1.242 and 0.978 times that of the hanging plate sensor, respectively. The corrosion rate of the galvanic sensor initially increases and subsequently decreases because the salt deposited onto the surface participates during the reaction, whereas that of the resistance sensor first increases and then decreases in an alternate period. X-ray photoelectron spectroscopy analysis show that the main components of the rust layer are α-FeOOH, Fe3O4 and γ-FeOOH, with α-FeOOH being the most abundant. Indoor simulation experiments show that with an increase in temperature, the corrosion products on the carbon steel surface change from granular to massive. This is because oxygen solubility in the thin-film liquid decreases, coupled with an increase in the rate of oxygen diffusion through the thin-film liquid to the carbon steel matrix. These factors facilitate the migration rates of Fe2+ and OH? in thin-film liquids and accelerates the electrode process and chemical reaction of the anode and cathode under the thin-film liquid. With the prolongation of corrosion time, the color of the carbon steel surface darkens gradually, transitioning from light yellow to reddish-brown and brown, and the corrosion products become dense and evenly distributed on the surface of the sample, relatively protecting the matrix. Because of the appearance of cracks and pits on rust layer surface, the corrosion rate determined using the sensor are higher than those obtained using the outdoor hanging plate. However, the corrosion rate of the galvanic sensor is closer to that of the hanging plate, indicating that the galvanic sensor is more suitable for use than the hanging plate in urban atmospheric environments. |
| Key words: resistance sensor galvanic sensor response surface model temperature and relative humidity coupling experiment dry / wet alternate simulation experiment |
|
|
|
|
