Improving the energy efficiency of high-frequency ozonators through AI-based automatic control systems

Abstract The current state of high-frequency ozonators is considered and the main problems related to energy consumption and productivity are identified. This paper addresses the development of an AI-based automatic control system to improve the energy efficiency of high-frequency ozonators. Machine learning algorithms were applied to process real-time data on ozone concentration, water flow, and electrical parameters. Experimental results showed that the implementation of the AI system reduced energy consumption by 15 – 18% and improved the signal-to-noise ratio by 22%. Furthermore, the AI system maintained ozone generation stability at ± 5%, optimizing energy usage. These findings highlight the need for efficient control systems in high-frequency ozonators and demonstrate their potential for improving performance on an industrial scale.

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