Multiobjective optimization of cyclone separators using genetic algorithm

Abstract

Multiobjective optimization of a set of N identical reverse-flow cyclone separators in parallel was carried out by using the nondominated sorting genetic algorithm (NSGA). Two objective functions were used: the maximization of the overall collection efficiency and the minimization of the pressure drop. Nondominated Pareto optimal solutions were obtained for an industrial problem in which 165 m3/s of air was treated. In addition, optimal values of several decision variables, such as the number of cyclones and eight geometrical parameters of the cyclone, are obtained. The study shows that the diameters of the cyclone body and the vortex finder, and the number of cyclones used in parallel, are the important decision variables influencing the optimal solutions. This study illustrates the applicability of NSGA in solving multiobjective optimization problems involving gas-solid separations.