This paper presents a new empirical model and a two-layer neural network approach for the determination
of optimum body diameter (OBD) of air cyclones. OBD values were calculated by help of a
MATLAB® algorithm for 505 different artificial scenarios given in a wide range of five main operating
variables. The predicted results obtained from each proposed approach were compared with the wellknown
Kalen and Zenz’s model. The computational analysis showed that the empirical model and neural
network outputs obviously agreed with the Kalen and Zenz’s model, and all the predictions proved to be
satisfactory, with a correlation coefficient of about 0.9998 and 1, respectively. The maximum diameter
deviations from Kalen and Zenz’s model were recorded as only 61.3 cm and 6 0.0022 cm for the proposed
model and NN outputs, respectively. In addition to proposed approaches, the pressure drop problem
was controlled using a MATLAB® algorithm, and results were obtained rapidly and practically for
varying data used in the cyclone design.