Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Abstract

Stainless steel (SS) is used in many industrial applications that demand superior corrosion resistance. Modeling its corrosion behavior in common structural and various operational scenarios is beneficial to provide wall-thickness (WT) information, thus leading to a predictive asset integrity regime. In this spirit, modeling of SS 316L corrosion behavior using artificial neural networks (ANNs) is presented, whereby saline water at different concentrations is flown through an elbow structure at different flow rates and salt concentrations. Voltage, current, and temperature data are recorded hourly using electric field mapping (EFM) pins installed on the elbow surface and used for training the model. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. Moreover, a concise account of the observed scale formation is also reported.‏