Application of Artificial Neural Networks on Predicting Corrosion Rates of Carbon Steel in Taiwan Industrial Zones

DOI

https://doi.org/10.2991/msam-17.2017.63How to use a DOI?

Keywords

atmospheric corrosion; artificial neural network; carbon steel

Abstract

This study employed artificial neural network (ANN) to develop a regional forecasting model to predict atmospheric corrosion rates of carbon steels within general industrial zones and coastal industrial zones in Taiwan. Analyzed data are based on the results of metal atmospheric corrosion rates monitoring project executed by The Institute of Harbor & Marine Technology Center in Taiwan. The result shows that sulfur dioxide deposition is the most significant factor to impact carbon steel corrosion rate in general industrial zones. However, for coastal industrial zones both sulfur dioxide deposition and chloride deposition are significant factors. The results reveal that the corrosion rates predicted by ANN have the most accurate performance. Furthermore, duplicating the extreme values of training set data of ANN can reduce the errors of corrosion rates' prediction. As for corrosion classification category predictions, the results show that ANN can accurately predict the cases for coastal industrial zones, but there are up to 24% of misjudgments for cases of general industrial zones.

Open Access

This is an open access article distributed under the CC BY-NC license.

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TY  - CONF
AU  - Hsiang-Teng Lin
AU  - Chien-Ming Lo
AU  - Min-Der Lin
PY  - 2017/03
DA  - 2017/03
TI  - Application of Artificial Neural Networks on Predicting Corrosion Rates of Carbon Steel in Taiwan Industrial Zones
BT  - 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 278
EP  - 282
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.63
DO  - https://doi.org/10.2991/msam-17.2017.63
ID  - Lin2017/03
ER  -