Modeling and multi-objective optimization of square cyclones using CFD and neural networks

Abstract

Increasing of collection efficiency ( η) and decreasing of the pressure drop (Δ p), simultaneously, are important purpose in the design of cyclone separators. In the present study, multi-objective optimization of square cyclones is performed at three steps. At the first step, collection efficiency ( η) and the pressure drop (Δ p) in a set of square cyclones 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 Δ p with respect to geometrical design variables. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of square cyclones considering two conflicting objectives, η and Δ p. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of square 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.