Development of nonlinear multi-parameter control models for optimizing ozone generation processes

Abstract

The object of this research is the ozone generation process based on corona discharge. This study addresses the limitations of conventional PID control systems in nonlinear and multi-parameter environments. A control model based on fuzzy logic principles was developed and validated through simulation and experimental testing on MATLAB and LabVIEW platforms. The model successfully maintained ozone output within the range of 85–120 mg/L with 95% stability, even under varying conditions of voltage (5–25 kV), humidity (30–70%), and gas flow rate (2–10 L/min). Additionally, energy consumption was reduced by 20%. These results are attributed to the fuzzy logic model’s ability to effectively account for nonlinear interdependencies among parameters. The proposed solution stands out for its flexibility, high precision, and energy efficiency, offering clear advantages over traditional methods. The developed model is well-suited for implementation in real-world industrial ozone generation systems, particularly in complex and dynamic operating environments.