Our last post introduced the separation processes mainly involved in industrial applications, and we focused on the Gas-Solid Cyclone separator. As we said, the cyclone separators primarily rely on centrifugal forces to separate solid particles from gaseous streams. These devices are designed for a wide range of operating conditions with a low cost in terms of investment and maintenance costs due to their simplicity of construction . Regarding this, primarily, all the designs are based on the Stairmand  and the Lapple  designs. The Stairmand design is high-efficiency with a high pressure-drop design. On the other hand, the Lapple design generates a lower pressure drop by reducing the separation efficiency compared to the Stairmand design.
Even when the Stairmand and Lapple designs are the base for cyclone new design, in the past years, it has been shown that the efficiency of these devices is influenced by different parameters and, some changes have been proposed. The most common studies focused on improving or characterize cyclone behavior consider the shift in geometry parameters as the main factor. These studies can be summarized as follows:
Inlet configuration: Iozia and Leith ; Lim et al. ; Zhao et al. ; Bernardo et al. ; Zhao et al. ; Elsayed and Lacor ; Su et al. ; Hsiao et al. ; Misilulia et al. .
Cyclone body confiiiguration: Kim and Lee ; Iozia and Leith ; Hoffmann et al. ; Xiang et al. ; Gimbun et al. ; Chuah et al. ; Xiang and Lee ; Brar et al. ; Hsiao et al. .
Vortex finder configuration: Kim and Lee ; Iozia and Leith ; Lim et al. ; Raoufi et al. ; Hsiao et al. .
Dust collector configuration: Kaya and Karagoz ; Elsayed and Lacor .
For more information about how to do these kinds of changes in Cyclone Geometry, please look at the following video.
However, most of these researches have focused only on evaluating the effect of a single geometric parameter over the cyclone behavior. Therefore, in the last years, some researchers have focused on doing multi-objective optimization by combing Design of Experiment (DOE) with various modeling methods such as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods (INCLUIR REFERENCIAS).