Data Mining Model for Prediction Effect of Corrosion Inhibition

Electrochemical impedance Nyquist tests have become a common technique to study corrosion inhibition behavior of steel. Methionine has been investigated as corrosion inhibitor for carbon steel (C-steel) in 1 M HCl solution using electrochemical impedance spectroscopy (EIS). Based on these experimental tests, the efficiency of the inhibitor increases with increase in the inhibitor concentration and decreases with increase in temperature. In this paper, a model based on neural networks is presented in order to obtain predictions of imaginary impedance based on the real part of the impedance as a function of inhibitor concentration and temperature. For the network, the learning algorithm, the hyperbolic tangent sigmoid transfer function, and the linear transfer function were used. The results based on correlation coefficient and root-mean-square show the utility of this tool to predict impedance values without requiring the use of EIS tests.