Neural networks for corrosion polarization curves prediction during inhibition by carboxyamide-imidazoline on a pipeline steel

This paper presents a predictive model for corrosion polarization curves using artificial neural network. This proposed obtains predictions of current in base of a corrosion inhibitor concentration and potential. The model takes into account the variations of inhibitor concentration over steel by thermo mechanical processing to decrease corrosion rate material. 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, which made it possible to predict 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 (R>0.985). The developed model can be used for the prediction of the current in short simulation time.