A predictive model for corrosion of carbon steel exposed to organic acids: Theory and validation in formic, azelaic and citric acid

塔菲尔方程 腐蚀 柠檬酸 有机酸 甲酸 材料科学 碳钢 化学 冶金 有机化学 电化学 电极 物理化学
作者
Elena Messinese,Marco Ormellese,A. Brenna
出处
期刊:Corrosion Science [Elsevier]
卷期号:235: 112160-112160 被引量:6
标识
DOI:10.1016/j.corsci.2024.112160
摘要

Organic acids can severely corrode metals, despite rarely reaching particularly low pH values. The mechanisms underlying the corrosion process in the presence of organic acids can be different and more complex with respect to the ones in the presence of strong acids. Since organic acids are commonly found in several industrial environments, they pose a serious threat that needs to be carefully addressed and investigated. Throughout the years, many research efforts have been put towards predictive modelling of corrosion. The Tafel-Piontelli model aims at becoming a universal and versatile model for acidic corrosion, able to adapt to different situations and predict corrosion rates of active metals exposed to all kinds of acidic environments. The model foundations are grounded in the principles of the corrosion process, regulated by the Tafel law. The efficacy of the model was tested on carbon steel samples immersed in solutions of weak acids with increasing proticity — formic, azelaic, and citric acid — at temperatures ranging from 20°C to 60°C and pH values from 3 to 4. The validation analysis was conducted using two primary tests: mass loss tests to assess corrosion rates and potentiodynamic polarization tests to determine the kinetic parameters of the cathodic and anodic reactions involved in the corrosion process. The results from the experiments are promising, confirming and extending the predictive capabilities of the model to the case of organic acids. These findings have broad implications for improving safety and durability in industrial applications where organic acids are prevalent, highlighting the model's potential to significantly enhance preventive strategies and material selection.
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