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

Abstract This 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.

[1]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[2]  Wen Yu,et al.  Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms , 2004, Inf. Sci..

[3]  Sung-Kwun Oh,et al.  Hybrid identification in fuzzy-neural networks , 2003, Fuzzy Sets Syst..

[4]  Robert S. Leiken,et al.  A User’s Guide , 2011 .

[5]  A. Guez,et al.  A Comparison of Three Nonlinear Controller Designs Applied to a Non-Adiabatic First-Order Exothermic Reaction in a CSTR , 1992, 1992 American Control Conference.

[6]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[7]  Vijander Singh,et al.  ANN-based estimator for distillation using Levenberg-Marquardt approach , 2007, Eng. Appl. Artif. Intell..

[8]  Frank L. Lewis,et al.  Identification of nonlinear dynamical systems using multilayered neural networks , 1996, Autom..

[9]  Françoise Lamnabhi-Lagarrigue,et al.  Nonlinear systems parameters estimation using radial basis function network , 2006 .

[10]  Dale E. Seborg,et al.  A Nonlinear Predictive Control Strategy Based on Radial Basis Function Networks , 1992 .

[11]  Okyay Kaynak,et al.  A comparative study of neural network structures in identification of nonlinear systems , 1999 .

[12]  Leang-San Shieh,et al.  A new approach for neural control of nonlinear discrete dynamic systems , 2005, Inf. Sci..

[13]  Khashayar Khorasani,et al.  Adaptive time delay neural network structures for nonlinear system identification , 2002, Neurocomputing.