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

Increasing of collection efficiency (η) and decreasing of the pressure drop (Δp), 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 (Δp) 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 Δp 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 Δp. 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.