EKF and ANFIS estimator design in multicomponent batch distillation columns

Date

2004-01-01

Author

Yildiz, Ugur
Güner, Evren
Özgen, Canan
Leblebicioğlu, Mehmet Kemal

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In the control of batch distillation columns, one of the problems is the difficulty of monitoring the compositions. This problem can be handled by estimating the compositions from readily available online temperature measurements using a state estimator. In this study, an extended Kalman Filter (EKF) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) state estimators that infer the product composition in a multicomponent batch distillation column (MBDC) from the temperature measurements are designed and tested using a batch column simulation. The designed EKF and ANFIS estimators are successfully used in the composition - feedback inferential control of MBDC operated under variable reflux-ratio policy with an acceptable deviation from the desired purity level of the products.

Subject Keywords

Adaptive Neurc-Fuzzy Inference System, Batch Distillation, Kalman Filter, Simulation, State Observer

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083208266&origin=inward
https://hdl.handle.net/11511/106520

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Department of Chemical Engineering, Conference / Seminar