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

Abstract

Stainless steel (SS) is widely employed in 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, an approach to model the corrosion behavior of SS 316L using artificial neural networks (ANNs) is developed, 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, which serve as training data for the ANNs. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. The results show the exceptional performance of the proposed single ANN model to predict WT. The error is calculated by comparing the estimated WT and actual measurement recorded, where the maximum error for each setting is range from 0.5363 to $0.7535\%$. RMSE and MAE values of each pin in every setting are also computed such that the maximum values of RMSE and MAE are 0.0271 and 0.0266, respectively. Moreover, a concise account of the observed scale formation is also reported. This comprehensive study contributes to a better understanding of SS 316L corrosion and offers valuable insights for developing efficient strategies to prevent corrosion in industrial environments. By accurately predicting WT loss using ANNs, this approach enables proactive maintenance planning, minimizing the risk of structural failures and ensuring the extended sustainability of industrial assets.