Evaluation of the effects of corrosion of carbon steel in acid mine drainages by using an explainable artificial intelligence model.
Year: 2025
Type: article
Abstract: One of the main problems faced by sulfide mining facilities is the corrosion of steel caused by acidic leachates, a process further accelerated by acidophilic bacteria that act as catalysts in the reaction. This study introduces an innovative Artificial Intelligence (AI)-based approach to assess the impact of acid mine drainage and to validate prior results obtained through classical statistical methods. A laboratory experiment was conducted in which S-235JR (EN 10025) carbon steel specimens were immersed for 26 days in four different solutions: two with a high Fe2+/Fe3+ ratio (one from a mine containing acidophilic bacteria and another synthetic solution devoid of bacteria), one from a mine with a high Fe3+/Fe2+ ratio, and a distilled water solution intended to simulate the effects of rainwater. The results reveal a more pronounced relative increase in Fe concentration in solutions with a higher initial Fe2+/Fe3+ ratio. All acidic solutions eventually reached anoxic conditions, with the rate of decline being slower in the solution without bacteria. A sharp decrease in Cu concentration was observed across all solutions, independent of the presence of bacteria or the Fe2+/Fe3+ ratio, and was attributed to the net acidity, which maintained Cu in solution for a longer period. In the synthetic solution without acidophilic bacteria, a gradual increase in pH was noted throughout the experiment, as the steel oxidation reactions occurred more slowly. In contrast, solutions containing acidophilic bacteria exhibited a more abrupt increase in pH during the initial hours of the experiment.
Source: Results in Engineering
Institution: Universidad de Huelva
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4407867932