Modeling of the electrochemical impedance spectroscopic behavior of passive iron using a genetic algorithm approach

In order to predict the general corrosion damage to metals and alloys, development of deterministic models and the acquisition of values for various model parameters are of paramount importance. In the present work, the Point Defect Model (PDM) was further developed to account for the properties of the passive film on pure iron in deaerated solutions. The model parameter values were extracted from the electrochemical impedance spectroscopic (EIS) data collected for iron in borate buffer solution [0.3 M H₃BO₃ + 0.075 M Na₂B₄O₇, pH = 8.15 at 21 C] + 0.001 M EDTA [Ethylenediaminetetraacetic acid, EDTA, disodium salt] at 21 C by optimization of the PDM on the experimental EIS data using an Genetically inspired, Differential Evolution Algorithm (GDEA). EDTA effectively suppresses the formation of the outer layer of the passive film, therefore rendering the barrier layer amenable to direct examination. Comparison of the experimental and calculated data demonstrates that the impedance model based on the PDM provides a good account of the growth of the passive film on iron and the extracted model parameters can be used to predict the corrosion evolution of the sample as a function of time.