Microalloyed pipeline steels mechanical resistance can be improved by dispersion
strengthening. The enhancement of steel dispersion strengthening by tempering at a suitable
temperature has been studied at various holding times at 3, 6, 8 and 10 hours. Depending on the
elapsed time, microalloying elements that were still located within steel iron lattice can be re-diffused,
thus developing different nanoparticle sizes, densities and distribution. The steel yield strength and
sulphide stress cracking resistance were significantly improved under sour environment. A systematic
electrochemical impedance spectroscopy (EIS) corrosion study was carried out. The objective of the
present work was to predict corrosion results from EIS collected data from the different steel tempering
times and exposure temperatures to sour environment (room temperature and 50 °C) by means of an
artificial neural network (ANN). 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.
Keywords: artificial neural network, sour corrosion, microalloyed steel, electrochemical impedance
spectroscopy