Genetic Optimization of Cyclone Geometry for Fine-Particle Separation

Abstract

Cyclone separators are pivotal components in industrial gas-solid separation processes, widely utilized due to their robust construction, lack of moving parts, and relatively low maintenance costs. However, the inherent limitation of conventional cyclone separators lies in their reduced collection efficiency for fine particles, particularly those smaller than five micrometers. Traditional design methodologies have relied heavily on empirical models and trial-and-error experimental approaches, which often fail to capture the complex, highly swirling turbulent flow dynamics internal to the separator. This paper presents a comprehensive investigation into the application of genetic algorithms combined with computational fluid dynamics to systematically optimize the geometric parameters of a cyclone separator specifically targeted at enhancing fine-particle separation. By parameterizing key structural dimensions, including the vortex finder diameter, cylinder height, cone length, and inlet dimensions, a vast design space is explored. A multi-objective fitness function is established to balance the maximization of fine-particle collection efficiency against the minimization of aerodynamic pressure drop, which dictates operational energy consumption. The study employs advanced turbulence modeling to evaluate the fitness of each computationally generated geometry across successive generations. The findings demonstrate that the biologically inspired evolutionary optimization process converges on a non-intuitive geometric configuration that significantly suppresses secondary flow patterns and short-circuiting phenomena. This research provides a robust, scalable framework for the automated design of highly efficient centrifugal separation equipment, contributing to both industrial process optimization and environmental particulate emission control.