A Combined Computational Fluid Dynamics and Artificial Neural Networks Model for Distillation Point Efficiency

Abstract

In this work a CFD-ANN model is developed to give the predictions of sieve tray point efficiency. The main objective has been to find the extent to which CFD can be used in combination with artificial neural network as a prediction tool for efficiencies of industrial trays. The model was tested against a wide range of tray geometries, operating conditions and binary systems of materials. CFD model was applied, as a virtual experiment tool for direct prediction of point efficiencies, using tray geometries and operating conditions for any binary system of liquids. The model results were in agreement to experimental data from literatures, shown that CFD-ANN model can be used as a powerful tool in distillation column design and analysis.