Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Modelling input data interactions for the optimization of artificial neural networks used in the... 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/approachThe 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.FindingsIn 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/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Anti-Corrosion Methods and Materials Emerald Publishing

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

Loading next page...
 
/lp/emerald-publishing/modelling-input-data-interactions-for-the-optimization-of-artificial-uocjRutVPf

References (22)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0003-5599
DOI
10.1108/acmm-07-2018-1976
Publisher site
See Article on Publisher Site

Abstract

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/approachThe 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.FindingsIn 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/valueThis 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.

Journal

Anti-Corrosion Methods and MaterialsEmerald Publishing

Published: Aug 9, 2019

Keywords: Inhibitors; Pitting; Modelling and prediction; Nuclear; Tensorflow

There are no references for this article.