Application of Artificial Neural Networks on Predicting Corrosion Rates of Carbon Steel in Taiwan Industrial Zones
Hsiang-Teng Lin, Chien-Ming Lo, Min-Der Lin
Available Online March 2017.
- https://doi.org/10.2991/msam-17.2017.63How to use a DOI?
- atmospheric corrosion; artificial neural network; carbon steel
- 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.
Cite this article
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 -