Artificial neural network estimator design for the inferential model predictive control of an industrial distillation column

Date

2004-09-15

Author

Bahar, A
Özgen, Canan
Leblebicioğlu, Mehmet Kemal
Halıcı, Uğur

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An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller (MPC), is developed for an industrial multicomponent distillation column. In the control of product compositions by a feedback control system, because of the difficulty of on-line measurements of compositions, temperature measurements can be utilized. The selection of the temperature measurement points for the inferential control is done by the help of singular value decomposition (SVD) analysis together with column dynamics information. A moving window ANN estimator is designed to estimate the product compositions from tray temperature measurements. The composition predictions are further corrected with the actual composition data in 30-min intervals. A multi input multi output (MIMO) MPC is used with the developed ANN estimator for the dual composition control of the column. The performance of the developed control system utilizing ANN estimator is tested considering set-point tracking and disturbance rejection performances for the unconstrained and constrained cases. It is observed that the controller utilizing ANN estimator is as good as the controller utilizing direct composition values.

Subject Keywords

Chemical-Plant

URI

https://hdl.handle.net/11511/32473

Journal

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

DOI

https://doi.org/10.1021/ie030585g

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

A. Bahar, C. Özgen, M. K. Leblebicioğlu, and U. Halıcı, “Artificial neural network estimator design for the inferential model predictive control of an industrial distillation column,” INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, pp. 6102–6111, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32473.