Determination of optimum body diameter of air cyclones using a new empirical model and a neural network approach

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 (R) 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 well-known 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 +/- 1.3 cm and +/- 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 (R) algorithm, and results were obtained rapidly and practically for varying data used in the cyclone design.