Application of Feed Forward and Recurrent Neural Network Topologies for the Modeling and Identification of Binary Distillation Column

Abstract

AbstractThis paper presents identification of artificial neural network model of a Binary Distillation Column (BDC). In this paper, the two most common topologies of artificial neural networks in the area of control are introduced: Feed forward neural network and recurrent neural networks. The training of neural network has been performed by the data set acquired from real 9-tray continuous BDC setup available in laboratory. The network model is composed of two layers. A hyperbolic tangent sigmoid function and a pure linear function have been utilized as activation functions in the first and the second layers, respectively. The developed neural network model has been validated by an extensive data set of practical data received from real BDC setup.