Artificial Neural Networks for Electrochemical Impedance Spectroscopy Sour Corrosion Predictions of Nano-modified Microalloyed Steels

Abstract

Micro alloyed steels mechanical properties can be modified by nano-modification based on aging heat treatments inducing different levels of nano precipitates on their surface microstructure. Under sour corrosion, electrochemical impedance spectroscopy (EIS) technique could serve to identify the modified micro alloyed steel corrosion properties. This paper present a predictive model for EIS-Ny quist curves using artificial neural networks (ANN) of micro alloyed steels under sour corrosion. For the ANN, an approach based on Levenberg–Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and a linear transfer function was used. The model takes into account of the variations of the real impedance, time and steel exposure temperature. The developed model can be used for prediction at short simulation times illustrating the utility of the ANN. On the validation data set, the simulations and the theoretical data tests were in good agreement with R2 > 0.98 for all experimental databases. These results suggest that ANN may play a key role in making lifetime predictions for components based on laboratory measurements.