This work aims to compare several algorithms for predicting the inhibition performance of localized corrosion. For this more than 400 electrochemical experiments were carried out in a corrosive solution containing an inorganic inhibitor. Pitting potential is used to indicate the performance of the inhibitor/oxidant mixture to prevent pitting corrosion.
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.
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