State estimation and inferential control for a reactive batch distillation column


Bahar A., ÖZGEN C.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.23, no.2, pp.262-270, 2010 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 2
  • Publication Date: 2010
  • Doi Number: 10.1016/j.engappai.2009.11.003
  • Journal Name: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.262-270
  • Keywords: Reactive Distillation, Batch Column, Optimization, State Estimation, Artificial Neural Networks, DESIGN, OPTIMIZATION

Abstract

An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. it is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary (C) 2009 Elsevier Ltd. All rights reserved.