State estimation and inferential control for a reactive batch distillation column

Date

2010-03-01

Author

Bahar, Almila
Özgen, Canan

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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

Subject Keywords

Reactive Distillation, Artificial Neural Networks, State Estimation, Optimization, Batch Column

URI

https://hdl.handle.net/11511/31119

Journal

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

DOI

https://doi.org/10.1016/j.engappai.2009.11.003

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

A. Bahar and C. Özgen, “State estimation and inferential control for a reactive batch distillation column,” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, pp. 262–270, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31119.