Modelling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion

Mohamed Nadir Boucherit (Unité de Recherche et Développement de l’Ingénierie Nucléaire, Algiers, Algeria)
Sid Ahmed Amzert (Centre de Recherche Nucléaire de Birine, Birine, Algeria)
Fahd Arbaoui (Centre de Recherche Nucléaire de Birine, Birine, Algeria)
Yakoub Boukhari (Université of Oum El Bouaghi, Ain Oussera, Algeria)
Abdelkrim Brahimi (Centre de Recherche Nucléaire de Birine, Birine, Algeria)
Aziz Younsi (Centre de Recherche Nucléaire de Birine, Birine, Algeria)

Anti-Corrosion Methods and Materials

ISSN: 0003-5599

Publication date: 1 July 2019

Abstract

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.

Keywords

Citation

Boucherit, M.N., Amzert, S.A., Arbaoui, F., Boukhari, Y., Brahimi, A. and Younsi, A. (2019), "Modelling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion", Anti-Corrosion Methods and Materials, Vol. 66 No. 4, pp. 369-378. https://doi.org/10.1108/ACMM-07-2018-1976

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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