System-Level Prediction and Optimization of Cyclone Separator Performance Using a Hybrid CFD-DEM-ANN Approach

Abstract

In this study, the separation performance of cyclone separators with different geometric configurations was investigated using a hybrid approach that combines Computational Fluid Dynamics, the Discrete Element Method, and Artificial Neural Networks. In the first stage, the flow field was solved using the Reynolds-Averaged Navier-Stokes equations together with the Reynolds Stress Model turbulence closure, and particle motion was evaluated in detail through DEM. To examine the effect of geometric parameters, the inlet aspect ratio, vortex finder diameter, and cylinder height were systematically assessed. The results revealed the formation of a pronounced Rankine-type vortex structure inside the cyclone and showed that secondary flow regions intensified as the vortex finder diameter and cylinder height increased, thereby reducing the separation efficiency. In the inlet section, an optimal aspect ratio was identified. In the second stage, an ANN model was developed to expand the limited dataset obtained from the CFD-DEM analyses. By optimizing the activation function and the number of neurons, the best performance was achieved with a ReLU-based neural network containing a single hidden neuron, reaching a test-set accuracy of approximately R2 approximate to 0.991 and an overall fit of R2 approximate to 0.895. The ANN model also captured interaction trends between flow velocity and geometry that could not be observed with the limited CFD dataset. This hybrid approach provides an effective and low-cost method for performance prediction and optimization in cyclone separator design.