Predictive models for determination of pitting corrosion versus inhibitor concentrations and temperature for radioactive sludge in carbon steel waste tanks

Statistical models were developed to predict the occurrence of pitting corrosion in carbon steel (CS) waste storage tanks exposed to radioactive nuclear waste. Levels of nitrite (NO2-) concentrations necessary to inhibit pitting at various temperatures and nitrate (NO3-) concentrations were determined experimentally via electrochemical polarization and coupon immersion corrosion tests. Models for the pitting behavior were developed based upon various statistical analyses of the experimental data. Feed-forward, artificial neural network (ANN) models, trained using the back-propagation of error algorithm, more accurately predicted conditions at which pitting occurred than the logistic regression models developed using the same data.