Improvement of mine water treatment technology using electric discharge-based ozonator with the application of artificial intelligence methods

Abstract This article examines the efficiency of using electric discharge-based ozonators in mine water treatment and explores their integration with artificial intelligence (AI) methods. Theoretical and experimental studies demonstrated that the concentration of heavy metals was reduced by up to 82%, while organic impurities decreased by up to 85%. AI algorithms were developed in MATLAB, and the process was simulated using COMSOL Multiphysics. The prototype of the AI-based control system processed real-time sensor data and stabilized ozone concentration within ± 5%, reduced energy consumption by 20 – 30%, and maintained treatment efficiency above 80%. The results showed that, compared to traditional control methods, the proposed approach significantly enhanced process stability and ensured environmental safety.

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