Modeling and Multi-Objective Optimization of Cyclone Vortex Finder using CFD and Neural Networks

Vortex finder is a key part of cyclone separator. In the present study, multi-objective optimization of vortex finder is performed at three steps. At the first step, collection efficiency (η) and the pressure drop (∆p) in a set of cyclones with different vortex finder shape are numerically investigated using CFD techniques. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, at the second step, for modeling of η and ∆p with respect to geometrical design variables. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of vortex finder considering two conflicting objectives, η and ∆p. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of cyclones can be discovered by Pareto based multi-objective optimization of the obtained polynomial meta-models. Such important optimal principles would not have been obtained without the use of both GMDH type neural network modeling and the Pareto optimization approach.