Optimization of ozone generator sizing and performance using response surface modeling and metaheuristic algorithms: a comparative study of HLOA and LCA

Abstract

This study investigates the optimization of a dielectric barrier discharge (DBD) cylindrical-shaped ozone generator, crucial for air purification and water treatment. The research focuses on the effects of voltage, electrode length, and discharge gap on ozone concentration, ozone generation rate, and energy efficiency, aiming to enhance performance while minimizing energy consumption. Systematic experiments were conducted by varying generator parameters at the APELEC Laboratory, Djillali Liabes University of Sidi-Bel-Abbes, Algeria, yielding a peak ozone concentration of 53.32 mg/L and an energy efficiency of 5.82 g/Wh. To enhance understanding and performance, response surface modeling (RSM) was employed to develop a mathematical model that elucidates the relationships between performance metrics and generator parameters. This model facilitated the identification of optimal parameter combinations to maximize ozone concentration and efficiency. In a novel approach, two advanced metaheuristic algorithms the liver cancer algorithm (LCA) and the horned lizard optimization algorithm (HLOA) were utilized. The HLOA achieved an improved ozone concentration of 57.35 mg/L and an energy efficiency of 5.88 g/Wh, surpassing the LCA’s results of 56.9 mg/L and 5.76 g/Wh. The results provide valuable insights for enhancing ozone generators, demonstrating the effectiveness of combining RSM with metaheuristic approaches. This hybrid method not only clarifies parameter relationships but also establishes a new benchmark for future research in ozone generator optimization.