Data Reduction of a Numerically Simulated Sugar Ext raction Process in Counter-current Flow Horizontal Extracto rs

Abstract

In this work, Response Surface Methodology (RSM) an d Artificial Neural Networks (ANN) were employed for the data reduction of a num erically simulated extraction process of sugar in an industrial RT2 extractor. Th e numerical model developed in OpenFOAM library was first validated using actual p lant data and its stability and sensitivity to the processing variables was tested. Then, the model was used to generate data of juice and pulp sugar concentrations as affe cted by the main processing parameters including draft, Silin number, and capac ity. The data were modelled using RSM and ANN. Both RSM and ANN were able to predict the data accurately, however, R 2 values obtained for ANN were slightly higher. Sinc e the numerical model can be time consuming to be solved for all data ranges, the reg ression equation obtained by the RSM method or the network created according to the ANN model can be utilized as fast and ready to use tools to optimize the extractor. Keywords: ANN, CFD, Mass transfer, Open FOAM, RSM.