Modeling and Pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms

Abstract

Both the pressure drop and the cut-off diameter are important performance parameters in the design of the cyclone separator. In this paper, a multi-objective optimization study of the gas cyclone separator is performed. In order to predict accurately the complex non linear relationships between the performance parameters (pressure drop and cut-off diameter) and the geometrical dimensions, two radial basis function neural networks (RBFNNs) are developed and employed to model the pressure drop and the cut-off diameter for cyclone separators. The artificial neural networks have been trained and tested by the experimental data available in literatures for the pressure drop and the Iozia and Leith model for the cut-off diameter. The results demonstrate that artificial neural networks can offer an alternative and powerful approach to model the cyclone performance parameters. The analysis indicates the significant effect of the vortex finder diameter Dx, the vortex finder length S, the inlet width b and the total height Ht. The response surface methodology has been used to fit a second-order polynomial to the RBFNN. The second-order polynomial has been used to study the interaction between the geometrical parameters. The two trained artificial neural networks have been used as two objective functions to get new optimal ratios for minimum pressure drop and minimum cut-off diameter using the multi-objective genetic algorithm optimization technique. Sometimes, the main concern is minimizing the pressure drop, so a single objective optimization study has been performed to obtain the cyclone geometrical ratio for minimum pressure drop. The comparison of numerical simulation of the new optimal design and the Stairmand design confirms the superior performance of the new design.