Prediction of total concentration for spherical and tear shape drops by using neural network

Abstract

In this study, the development of an alternative approach based on the Artificial Intelligent technique called Artificial Neural Network (ANN) was carried out. This report presents a new application of ANN techniques to the modeling of prediction total concentration of drops in the Rotating Disc Contactor Column (RDC). The ANN was trained with the simulated data based on spherical and tear-shaped drops, which consider ten classes volume of drops. The comparison result between Neural Network output and Mathematical Model output is presented. With 4 hidden nodes, the Neural Network models are able to generate the smallest MSE for each ten classes volume of drops. Then the neural network model is then being applied to the combination for all shape drops, which are spherical and tear shape drops as the inputs. The Neural Network models are able to predict 400 simulated data for combination spherical and tear shape drops with MSE error value 68482.6?E. The results with the smallest MSE presented in this paper shows that the Neural Network Model works successfully in prediction total concentration of multiple shape drops in ten classes volumes.