Modeling and multi-objective optimization of cyclone separators using CFD and genetic algorithms

In the present study, multi-objective optimization of cyclone separators is performed at three steps. At the first step, pressure drop (Δp) and the cut-point (D50) in a set of cyclone separators 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 Δp and D50 with respect to geometrical design variables. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of cyclone separators considering two conflicting objectives, Δp and D50.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.