The use of artificial neural networks for modelling pitting corrosion behaviour of EN 1.4404 stainless steel in marine environment: data analysis and new developments

Jiménez‐Come, María Jesús; Martín, María de la Luz; Matres, V.; Baladés, J. Daniel Mena

doi:10.1515/corrrev-2019-0095

Cited by 11 publications

(5 citation statements)

References 45 publications

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“…In Figure 7, it can be observed that different ANN models with different neurons in the hidden layer provide higher R 2 of 0.9999, lower RMSE of 0.0002, and MAPE of 0.0171. Hence, this 3-3-1 The close linear pattern between the ANN predicts the values for the performance parameters, and the experimental dataset shows the adjacency of the model (Jiménez-Come et al, 2020). In order to determine the relative value of each input parameter, the sensitivity analyses have been performed.…”

Section: Resultsmentioning

confidence: 77%

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Zeraati

Abbasi²,

Ghaffarzadeh

et al. 2022

Front. Mater.

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Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate coating. This study described a new reliability simulation framework to determine the corrosion behavior of coating using a gene artificial neural network (ANN) to estimate the corrosion current of the coating. The input parameters of the model are temperature, pH of electroplating bath, current density, and Ni2+ concentration, and corrosion current defined as output. The effectiveness and accuracy of the model were checked by utilizing the absolute fraction of variance (R2 = 0.9999), mean absolute percentage error (MAPE = 0.0171), and root mean square error (RMSE= 0.0002). This is determined using the genetic algorithm (GA) and the optimum practice condition.

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Section: Resultsmentioning

confidence: 77%

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Zeraati

Abbasi²,

Ghaffarzadeh

et al. 2022

Front. Mater.

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“…In this study, the use of the ANN model allows us to determine the corrosion state of the sample under different conditions simulating biogas environments. Based on our experience in corrosion modelling [43,44], different ANN model configurations were proposed to achieve the established objectives: Objective 1-To develop an ANN model capable of predicting the corrosion state of stainless steel involved in biogas production without the need for microscopic analysis of the analyzed sample after electrochemical tests.…”

Section: Introductionmentioning

confidence: 99%

Corrosion Behaviour Modelling Using Artificial Neural Networks: A Case Study in Biogas Environment

Jiménez-Come,

González Gallero,

Álvarez Gómez

et al. 2023

Metals

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The main objective established in this work was to develop a model based on artificial neural networks (ANNs) to predict the corrosion status of stainless steel involved in biogas production, analyzing the influence of the material composition and the breakdown potential value. To achieve this objective, an ANN model capable of predicting the corrosion status of the material without the need to perform microscopic analysis on the material surface was proposed. The applicability of the corrosion models was verified via the experimental data considering different factors such as stainless steel composition, biogas environments simulated by artificial solution, temperature, surface finish, and the breakdown potential of the passive layer of stainless steel obtained from electrochemical tests. The optimal prediction performance shown by the model in terms of specificity and sensitivity values were 0.969 and 0.971, respectively, obtaining an accuracy of 0.966. Furthermore, analyzing the influence of the breakdown potential on corrosion modelling, an alternative model was presented capable of predicting the corrosion status automatically, without the need to resort to electrochemical tests for new conditions. The results demonstrated the utility of this technique to be considered in design and maintenance planning tasks for stainless steel structures subjected to localized corrosion in biogas production.

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“…Moreover, the soil parameters were subjected to sensitivity analysis. ANNs were utilized to predict the pitting corrosion of SS 316L and EN 1.4404, while environmental conditions were taken into account in 26 and 27 . The results in the marine environment, in particular, were later reported.…”

Section: Introductionmentioning

confidence: 99%

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

Memon

Imran

Alhems

2023

Preprint

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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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The use of artificial neural networks for modelling pitting corrosion behaviour of EN 1.4404 stainless steel in marine environment: data analysis and new developments

Cited by 11 publications

References 45 publications

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Corrosion Behaviour Modelling Using Artificial Neural Networks: A Case Study in Biogas Environment

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

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