Evaluation of the effects of corrosion of carbon steel in acid mine drainages by using an explainable artificial intelligence model.

• A major problem in sulfide mining facilities is the corrosion of steel caused by acidic leachates. • The impact of acid mine drainage on carbon steel was evaluated. • This study presents an innovative approach based on Artificial Intelligence. • In the laboratory, carbon steel specimens were immersed in four different solutions. One of the main problems faced by sulfide mining facilities is the corrosion of steel caused by acidic leachates, a process that is further accelerated by the presence of acidophilic bacteria that act as catalysts in the reaction. This study presents an AI-based approach to assess acid mine drainage as a result of corrosion, adding novelty to the study by using it alongside Fuzzy Logic, obtaining qualitative models based on fuzzy rules that corroborate and expand on the contributions of statistical techniques. In a laboratory experiment, S-235JR (EN 10025) carbon steel samples were immersed for 26 days in four solutions: two with a high Fe 2+ /Fe 3+ ratio, one from a mine with acidophilic bacteria (with an initial Fe 2+ concentration of 1390 mg/L) and one synthetic without bacteria (Fe 2+ concentration of 1961 mg/L); one from a mine with a high Fe 3+ /Fe 2+ ratio (with an initial Fe 2+ concentration of 0.10 mg/L) and distilled water simulating rain effects. Results showed a greater increase in Fe concentration in solutions with a higher initial Fe 2+ /Fe 3+ ratio (with an increase rate higher than 2). All acidic solutions eventually became anoxic, with a slower transition in the bacteria-free solution. Cu concentration dropped sharply in all cases, regardless of bacteria presence or Fe ratios, likely because the net acidity kept Cu in solution for a longer time. In the synthetic solution without bacteria, pH gradually increased as steel oxidation proceeded more slowly. In contrast, solutions with bacteria showed a sharp pH rise in the early hours of the experiment.

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Publication Details

Journal
Results in Engineering
Published
2025-02-23
DOI
https://doi.org/10.1016/j.rineng.2025.104380
Citations
1
Primary Topic
Oil and Gas Production Techniques
Type
article
Field-Weighted Citation Impact
1.15
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article

Evaluation of the effects of corrosion of carbon steel in acid mine drainages by using an explainable artificial intelligence model.

Aguasanta Miguel Sarmiento, M. L. de la Torre, José Miguel Dávila Martín, J. Aroba
1 citations
Results in Engineering
Oil and Gas Production Techniques
1.15
article

Evaluation of the effects of corrosion of carbon steel in acid mine drainages by using an explainable artificial intelligence model.

Aguasanta Miguel Sarmiento, M. L. de la Torre, José Miguel Dávila Martín, J. Aroba
article en
1 citations

Abstract

• A major problem in sulfide mining facilities is the corrosion of steel caused by acidic leachates. • The impact of acid mine drainage on carbon steel was evaluated. • This study presents an innovative approach based on Artificial Intelligence. • In the laboratory, carbon steel specimens were immersed in four different solutions. One of the main problems faced by sulfide mining facilities is the corrosion of steel caused by acidic leachates, a process that is further accelerated by the presence of acidophilic bacteria that act as catalysts in the reaction. This study presents an AI-based approach to assess acid mine drainage as a result of corrosion, adding novelty to the study by using it alongside Fuzzy Logic, obtaining qualitative models based on fuzzy rules that corroborate and expand on the contributions of statistical techniques. In a laboratory experiment, S-235JR (EN 10025) carbon steel samples were immersed for 26 days in four solutions: two with a high Fe 2+ /Fe 3+ ratio, one from a mine with acidophilic bacteria (with an initial Fe 2+ concentration of 1390 mg/L) and one synthetic without bacteria (Fe 2+ concentration of 1961 mg/L); one from a mine with a high Fe 3+ /Fe 2+ ratio (with an initial Fe 2+ concentration of 0.10 mg/L) and distilled water simulating rain effects. Results showed a greater increase in Fe concentration in solutions with a higher initial Fe 2+ /Fe 3+ ratio (with an increase rate higher than 2). All acidic solutions eventually became anoxic, with a slower transition in the bacteria-free solution. Cu concentration dropped sharply in all cases, regardless of bacteria presence or Fe ratios, likely because the net acidity kept Cu in solution for a longer time. In the synthetic solution without bacteria, pH gradually increased as steel oxidation proceeded more slowly. In contrast, solutions with bacteria showed a sharp pH rise in the early hours of the experiment.

Results in EngineeringVol. 25
Universidad de Huelva (ES)
Openalex Percentile: Top 26%
Oil and Gas Production Techniques
1.15
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