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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

PurposeThe evolution of a semi‐open cooling circuit of a nuclear reactor was monitored over a two year period. The work aims to provide orientation elements for preventive procedures against localised corrosion.Design/methodology/approachThe water of the circuit was analysed in stagnation and in circulation, at various sampling points. The rust was analysed by neutron diffraction and the surface quality of the steel was checked by microscopic observations.FindingsThe obtained results did not confirm the presence of rust in iron compounds supported by chlorine, such as the Akaganeite, β‐FeOOH. In addition, chemical analysis of water showed that, after two years, the increase of chlorine concentration and water conductivity remained weak. Moreover, the pH was maintained within values favourable rather to the passivation of the steel.Practical implicationsIt was deduced through this work that the dosing of the circuit with chlorine was not sufficient that it should require an annual replacement of the water.Originality/valueThe originality of this work resides in the evaluation of a semi‐open coolant circuit in service for ten years and located in an area subjected to seasonal sand winds.

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Purpose 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. Design/methodology/approach 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′. Findings 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. Originality/value 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.

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