Use of neural network for modeling of liquid-liquid extraction process in the RDC column

Abstract

Several Mathematical Models have been developed for processes involving Rotating Disc Contactor (RDC) Column. These models indicated that the hydrodynamic and the mass transfer processes are important factors for the column performances. Usually, the mathematical simulation models describing the processes in the column are very complex. It also needs excessive computer time to produce simulation data for further analysis. Therefore, an alternative approach based on Artificial Neural Network is considered to assist in speeding up the simulation process. This paper presents a new application of Artificial Neural Network (ANN) techniques to the modeling of the liquid-liquid extraction process in the RDC Column. In this work, the ANN was trained with the simulated data obtained from Arshad (2000). The Neural Network models are able to generate 128 simulated data for RDC column with RMS error value of 1.0E-07. The comparison between Neural Network output and Mathematical Model(2000) output is also presented.