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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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