Long-term forecast of corrosion mass losses of technically important metals in various world regions using a power function

Panchenko, Yu. M.; Маршаков, А. И.; Igonin, T. N.; Kovtanyuk, V. V.; Nikolaeva, L. A.

doi:10.1016/j.corsci.2014.07.049

Cited by 59 publications

(44 citation statements)

References 29 publications

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“…Figure describes the relationship between thickness loss (µm) and exposure time (months) in log–log coordinates. Previous studies reported that such a relationship follow the well‐known logarithmic equation, as follows:

D = A t^{n} or \log D = \log A + n \log t,

where D is thickness loss (µm), t is exposure time (months), and A and n are constants. The value of n was proved to reflect the characteristics of rust and atmospheric corrosion kinetics.…”

Section: Resultsmentioning

confidence: 99%

Outdoor atmospheric corrosion of carbon steel and weathering steel exposed to the tropical‐coastal climate of Thailand

Pålsson

Wongpinkaew

Khamsuk

et al. 2019

Materials & Corrosion

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This study focuses on atmospheric corrosion of carbon steel and weathering steel exposed at two different locations with dissimilar meteorological parameters and airborne pollutants in Thailand. The samples are subjected to an outdoor atmosphere for up to 36 months at Rayong, close to the Gulf of Thailand, and Phangnga, near the Andaman Sea. Thickness loss (µm), corrosion rate (µm/year) together with corrosion product morphology and composition are systematically analyzed using X-ray diffraction and a scanning electron microscope. Corrosion resistance of the tested steels exposed at both locations is discussed based on the above-mentioned parameters and calculated corrosion kinetics. The results indicate that the total time of wetness, amount of rainfall and chloride deposition rate play an important role in corrosion behavior of the tested steels. Alloying elements, copper, chromium and nickel, are shown to improve corrosion resistance of the samples when exposed at the location with a higher chloride concentration. K E Y W O R D Satmospheric corrosion, carbon steel, corrosion rate, thickness loss, weathering steel

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D = A t^{n} or \log D = \log A + n \log t,

where D is thickness loss (µm), t is exposure time (months), and A and n are constants. The value of n was proved to reflect the characteristics of rust and atmospheric corrosion kinetics.…”

Section: Resultsmentioning

confidence: 99%

Outdoor atmospheric corrosion of carbon steel and weathering steel exposed to the tropical‐coastal climate of Thailand

Pålsson

Wongpinkaew

Khamsuk

et al. 2019

Materials & Corrosion

View full text Add to dashboard Cite

show abstract

“…According to the corrosion knowledge, the whole atmospheric corrosion process can be divided into two stages: The initial stage and stationary stage [32]. The former refers to the early stage of corrosion.…”

Section: Variable Importance Ranks According To the Modelmentioning

confidence: 99%

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

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The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

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“…Corrosion rates under full cycle test conditions were fitted using power function, which has been proven to be effective and accurate for corrosion study [15]. The corrosion rate formulas under oxygenated stirring and stationary conditions were:

\begin{array}{l} v_{s t i r r i n g} = 1.013 t^{- 0.653}, \begin{matrix}  \end{matrix} R^{2} = 0.992 (O x y g e n a t e d) \\ v_{s t a t i o n a r y} = 0.995 t^{- 0.653}, \begin{matrix}  \end{matrix} R^{2} = 0.984 (S t a t i o n a r y) \end{array}

where

R^{2}

was the goodness of fit coefficient.…”

Section: Corrosion Prediction and Discussionmentioning

confidence: 99%

“…Corrosion rates of the entire cycle under different test conditions were fitted using power function [15], and the test results under highest and lowest temperatures conditions were selected for comparison. Corrosion rate formulas under various temperature conditions were as follows:

\begin{array}{l} v_{30 ℃} = 1.020 t^{- 0.708}, \begin{matrix}  \end{matrix} R^{2} = 0.986 \\ v_{45 ℃} = 0.955 t^{- 0.745}, \begin{matrix}  \end{matrix} R^{2} = 0.992 \\ v_{60 ℃} = 0.985 t^{- 0.774}, \begin{matrix}  \end{matrix} R^{2} = 0.995 \end{array}

…”

Section: Corrosion Prediction and Discussionmentioning

confidence: 99%

Accelerated Degradation Test and Predictive Failure Analysis of B10 Copper-Nickel Alloy under Marine Environmental Conditions

Sun

Yao³

et al. 2015

Materials

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This paper studies the corrosion behavior of B10 copper-nickel alloy in marine environment. Accelerated degradation test under marine environmental conditions was designed and performed based on the accelerated testing principle and the corrosion degradation mechanism. With the prolongation of marine corrosion time, the thickness of Cu2O film increased gradually. Its corrosion product was Cu2(OH)3Cl, which increased in quantity over time. Cl− was the major factor responsible for the marine corrosion of copper and copper alloy. Through the nonlinear fitting of corrosion rate and corrosion quantity (corrosion weight loss), degradation data of different corrosion cycles, the quantitative effects of two major factors, i.e., dissolved oxygen (DO) and corrosion medium temperature, on corrosion behavior of copper alloy were analyzed. The corrosion failure prediction models under different ambient conditions were built. One-day corrosion weight loss under oxygenated stirring conditions was equivalent to 1.31-day weight loss under stationary conditions, and the corrosion rate under oxygenated conditions was 1.31 times higher than that under stationary conditions. In addition, corrosion medium temperature had a significant effect on the corrosion of B10 copper sheet.

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Long-term forecast of corrosion mass losses of technically important metals in various world regions using a power function

Cited by 59 publications

References 29 publications

Outdoor atmospheric corrosion of carbon steel and weathering steel exposed to the tropical‐coastal climate of Thailand

Outdoor atmospheric corrosion of carbon steel and weathering steel exposed to the tropical‐coastal climate of Thailand

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Accelerated Degradation Test and Predictive Failure Analysis of B10 Copper-Nickel Alloy under Marine Environmental Conditions

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