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In this study, we optimized the shape of the surface aerator that will be installed in a biological reactor using the response surface method. Response surfaces of mass flow rate, impeller torque, mass flow rate per impeller torque are generated and used to track the optimum shape of the aerator. MOGA(Multi-Objective Genetic Algorithm)method is adopted to find the optimum results. By increasing the mass flow rate per impeller torque, increase of oxygen supply efficiency to a reactor is anticipated. To verify the usability of the surface aerator, PIV measurements on flow fields inside a scale-downed biological reactor model are carried out.


In this study, we optimized the shape of the surface aerator that will be installed in a biological reactor using the response surface method. Response surfaces of mass flow rate, impeller torque, mass flow rate per impeller torque are generated and used to track the optimum shape of the aerator. MOGA(Multi-Objective Genetic Algorithm)method is adopted to find the optimum results. By increasing the mass flow rate per impeller torque, increase of oxygen supply efficiency to a reactor is anticipated. To verify the usability of the surface aerator, PIV measurements on flow fields inside a scale-downed biological reactor model are carried out.