CFD-Based Multi-Objective Optimization of a Cyclone Separator Using Response Surface Methodology, NSGA-II, and Physical Prototype Validation

Cyclone separators are widely used for gas–solid separation; however, improving separation efficiency generally increases pressure drop, making cyclone design a challenging multi-objective optimization problem. This study aims to optimize a high-efficiency Stairmand cyclone separator by simultaneously maximizing separation efficiency and minimizing pressure drop through a CFD-based optimization framework integrating Computational Fluid Dynamics (CFD), Design of Experiments (DOE), Response Surface Methodology (RSM), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization was performed using two geometric design variables: the vortex finder length (Lv) and the spigot diameter (Du). CFD simulations generated the data required to construct surrogate models, enabling rapid evaluation of candidate geometries during the optimization process. The Pareto-optimal solutions obtained with NSGA-II were subsequently verified using independent CFD simulations before manufacturing the selected configuration as a physical prototype. The optimized cyclone achieved a separation efficiency of approximately 93% while maintaining a low pressure drop of about 60 Pa. Experimental testing showed excellent agreement with the numerical predictions, with a deviation of only 1.51% in separation efficiency, confirming the reliability of the proposed optimization framework. The complete validation chain, from CFD simulation and surrogate-assisted optimization to prototype fabrication and laboratory testing, demonstrates that the proposed methodology provides a practical and reliable approach for the multi-objective design and optimization of industrial cyclone separators.