Abstract:Stainless steel is known for its superior corrosion resistance in industrial applications. In this work, corrosion modeling of stainless steel 316L is presented using artificial neural networks. The experimental setup consists of a loop containing stainless steel elbow with simulated seawater of known concentration continuously flowing at a specific flow rate, thus allowing to study the effect of flow dynamics and salt concentration on corrosion. Electric field mapping setup is used to collect the voltage and … Show more
“… Figure 4 Spectroscopic analysis of extracted scale deposit; ( a ) SEM-EDS; ( b ) XRF; ( c ) FT-IR; ( d ) XRD. The figure was reproduced with permission from our previous work 33 . …”
Section: Methodsmentioning
confidence: 99%
“…However, it was developed for wall thinning prediction with a single flow rate and salt concentration. By extending the results in 33 , a more general ANN model SS 316L elbow flown by saline water with various flow rates and concentrations is studied in this paper. The target is to have a single ANN model representing the corrosion behavior.…”
Section: Introductionmentioning
confidence: 99%
“…The methodology finds a wide range of applications, including the food industry, nuclear reactors, water desalination, and more. Recently, an initial result of the ANN model for WT of SS 316L elbow as the section of a running saline water loop was reported by the authors 33 . However, it was developed for wall thinning prediction with a single flow rate and salt concentration.…”
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 $$0.7535\%$$
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.
“… Figure 4 Spectroscopic analysis of extracted scale deposit; ( a ) SEM-EDS; ( b ) XRF; ( c ) FT-IR; ( d ) XRD. The figure was reproduced with permission from our previous work 33 . …”
Section: Methodsmentioning
confidence: 99%
“…However, it was developed for wall thinning prediction with a single flow rate and salt concentration. By extending the results in 33 , a more general ANN model SS 316L elbow flown by saline water with various flow rates and concentrations is studied in this paper. The target is to have a single ANN model representing the corrosion behavior.…”
Section: Introductionmentioning
confidence: 99%
“…The methodology finds a wide range of applications, including the food industry, nuclear reactors, water desalination, and more. Recently, an initial result of the ANN model for WT of SS 316L elbow as the section of a running saline water loop was reported by the authors 33 . However, it was developed for wall thinning prediction with a single flow rate and salt concentration.…”
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 $$0.7535\%$$
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.
“…However, it was developed for wall thinning prediction with a single flow rate and salt concentration. By extending the results in 19 , a more general ANN model SS 316L elbow flown by saline water with various flow rates and concentrations is studied in this paper. The target is to have a single ANN model representing the corrosion behavior.…”
Section: Introductionmentioning
confidence: 99%
“…Some 2D grayscale images were presented to illustrate the corrosion morphology. Recently, an initial result of the ANN model for WT of SS 316L elbow as the section of a running saline water loop was reported by the authors 19 . However, it was developed for wall thinning prediction with a single flow rate and salt concentration.…”
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.
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