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The purpose of this study is to confirm the idea that observing the electrochemical data of a steel polarized around its open circuit potential can provide insight into its performance against pitting corrosion. To confirm this idea a two-step work was carried out. The authors collected electrochemical data through experiments and exploited them through machine learning by building neural networks capable of predicting the behaviour of the steel against the pitting corrosion.

The electrochemical experiments consist in plotting voltammograms of the steel in chemical solutions of various degrees of corrosiveness. For each experiment, the authors observe how the open-circuit potential evolves over a period of 1 min, and following this, the authors observe the current evolution when they impose a potential scan that starts from the open-circuit potential. For each of these situations, the pitting potential Epit is noted. The authors then build different artificial neural networks, which after learning, can, by receiving electrochemical data, calculate a pitting potential Epit′. The performance of the neural networks is evaluated by the correlation of Epit and Epit′.

Through this work, different types of networks were compared. The results show that recurrent or convolutional networks can better capture the temporal nature of the input data.

The results of this work support the idea that the measurable electrochemical data around the free potential of a material can be correlated with its behaviour at more anodic potentials, particularly the initiation of pits.

This paper aims to predict the localized corrosion resistance by the application of artificial neural networks. It emphasizes the importance to take into account the relationships between the physical parameters before presenting them to the network.

The work was conducted in two phases. At the beginning, the authors executed an experimental program to measure pitting corrosion resistance of carbon steel in an aqueous environment. More than 900 electrochemical experiments were conducted in chemical solutions containing different concentrations of pitting agents, corrosion inhibitors and oxidant reagents. The obtained results were collected in a table where for a combination of the experimental parameters corresponds a pitting potential Epit obtained from the corresponding electrochemical experiment. In the second step, the authors used the experimental data to train different artificial neuron networks for predicting pitting potentials.

In this step, the authors considered the relationships that the chemical parameters are likely to have between them. Two types of relationships were taken into account: chemical equilibria which are controlled by the pH and the synergistic relationships that some corrosion inhibitors may have when they are in the presence of a chemical oxidant.

This comparative study shows that adjusting the input data by considering the physical relationships between them allows a better prediction of the pitting potential. The quality of the prediction, quantified by a regression factor, is qualitatively confirmed by a statistical distribution of the gap between experimental and calculated pitting potentials.

The purpose of this paper is to illustrate the usefulness of inhibitors for the prevention of localised corrosion of carbon steel in a low‐aggressive medium. The efficiencies of two inorganic non‐toxic inhibitors are compared, associated with an oxidant.

Many experiments were conducted. For each experiment, a solution was prepared with different concentrations of pitting agent, inhibitor and oxidant. The performance was then estimated by the pitting potential taken from the voltammograms of carbon steel obtained with each solution.

The results show that the efficiency of molybdate and tungstate were comparable. The presence of iodate, which plays an oxidizing role, can be synergistic to the inhibitor but harmful if the concentration ratio is not adequate.

The interest in the use of an oxidant is that it makes it possible to reduce the inhibitor concentration, which limits the pH increase and prevents scale deposition.

This work provides useful guidance in the localised corrosion prevention of a semi‐open cooling circuit subject to seasonal sand‐storms. The obtained results from the many experiments carried out were compiled using neural networks for performance prediction.