Neural Network for Dispersion Strengthened Microalloyed Steel Sour Corrosion from Electrochemical Impedance Spectroscopy Laboratory Measurements

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 3, 6, 8 and 10 hours. Depending of the elapsed time microalloying elements that were still located within steel iron lattice can be re-diffused developing different nanoparticles size, densities and distribution. The steel yield strength and sulphide stress cracking resistance were significantly improved under sour environment. Then 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 a artificial neural network (ANN) For the ANN, an approach based on Levenberg–Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and linear transfer function was used. The model takes into account 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, simulations and theoretical data test were in good agreement with an R² > 0.98 for all experimental data base. These results suggest that ANN may play a key role in making lifetime predictions for components based on laboratory measurements.