Neural networks for Nyquist plots prediction during corrosion inhibition of a pipeline steel

Abstract

This paper presents a predictive model for electrochemical impedance Nyquist plots using artificial neural network. The proposed model obtains predictions of imaginary impedance based on the real part of the impedance as a function of time. The model takes into account the variations of the real impedance and immersion time of steel in a corrosive environment, considering constant carboxyamido-imidazoline corrosion inhibitor concentrations (5 and 25 ppm). For the network, the Levenberg–Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer function, and the linear transfer function were used. The best-fitting training data set was obtained with five neurons in the hidden layer for 5 ppm of inhibitor and two neurons in the hidden layer for 25 ppm of inhibitor, which made it possible to predict the efficiency with accuracy at least as good as that of the theoretical error, over the whole theoretical range. On the validation data set, simulations and theoretical data test were in good agreement with an R value of 0.984 for 5 ppm and 0.994 for 25 ppm of inhibitor. The developed model can be used for the prediction of the real and imaginary parts of the impedance as a function of time for short simulation times.