ANN-Based Prediction of Corrosion Behavior of Alloy 600: Implications for an Anti-Corrosion Coating Design in PWSCC Environments

Abstract

The modeling of the corrosion rate of Alloy 600 in primary water stress corrosion cracking conditions (PWSCC) is a challenging task for existing as well as new structures due to the wide deviation of its composition across the worldwide PWSCC environment. The major parameters influencing the rate are temperature, stress intensity factor, pH, conductivity, ECP, Yield strength, B3(OH)3, and LiOH. The individual effects of these parameters on corrosion are known to some extent; however, the combined effect of these parameters together is complex, nonlinear, and unpredictable. Herein, we developed an Artificial Neural Network to predict the corrosion crack growth rate for any combination of the above five parameters and to better understand the effects of these parameters jointly on corrosion behavior. Three-dimensional mappings clearly reveal the complex interrelationship between the temperature and stress intensity factor at different variables, and the effect of the variables rather than a single variable on the corrosion rate of Inconel alloy 600 in PWSCC conditions. Moreover, the index of relative importance for these variables has also been presented providing deep insights for anti-corrosion coating designs in PWSCC environments.