Hydrodynamic simulation of spinning cone columns using artificial neural networks

In this research the effect of tray speed, pressure drop and cone spacing for spinning cone columns have been examined using artificial neural network. To obtain this objective, Radial BasisFunction (RBF) neural network structure and least mean squares (LMS) training algorithm has beenutilized. The findings of this study reveal that the predictions of this work are much accurate than those obtained from the existing empirical correlation. There also exists a good compatibility between the pressure drop values predicted from the present study and the experimental data in wet state. From the scheme adopted in this work, the spinning cone column capacity at different operating conditions could be estimated more accurately than the existing correlations.

Keywords:

pressure drop , neural networks , Radial Basis Function (RBF) , spinning cone column