Multi-objective shape optimization of vortex finders in cyclone separators using response surface methodology and genetic algorithms

Abstract

Multi-objective optimization of vortex finders is performed using Response Surface Methodology (RSM) and Genetic Algorithms (GA) for enhancing the performance of the standard Stairmand cyclone separator. Three diameters at different axial locations and the distance between these locations are optimized for enhancing the performance of the cyclone separator. This approach provides flexibility to the optimization process as the vortex finder is free to assume any shape (convergent or divergent) according to changes in the three diameters at different locations of the vortex finder. Optimization is carried out using five independent parameters and two dependent variables, Euler number and collection efficiency. The optimization results confirm that the present strategy is capable of optimizing the vortex finder for better performance than the standard Stairmand model. Fourteen optimal vortex finders have been obtained in the proposed work to select the required conflicting performance parameters. Genetic algorithms provide better solutions than RSM. Optimal vortex finders are stepped divergent or convergent-divergent in nature.