Abstract Gas cyclones have numerous industrial applications. Typically, each cyclone has a dustbin to collect the trapped particles and the dimensions of the dustbin affect the cyclone performance. This paper aims to optimize the dustbin geometry via numerical simulations. The surrogate-based optimization approach has been applied in this study. The Latin-hyper cube sampling plan is used to generate thirty test cases. An artificial neural network with radial basis function has been used as a surrogate model trained by the CFD simulations. Here three design parameters (the dipleg length, the dustbin height, and the dustbin diameter) and two performance parameters, namely, the Euler number and the Stokes number are used. The fitted surrogate model shows that the variations of the dustbin geometry have a larger effect on the Stokes number than that on the Euler number. Both single-objective and bi-objective optimization studies are carried out using the artificial neural network. It is shown that the resulting optimum design of the dustbin and the dipleg leads to better performance than the conventional cyclones.
