Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

Farouq S. Mjalli, Sameer Al-Asheh

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.

Original languageEnglish
Pages (from-to)1191-1200
Number of pages10
JournalChemical Engineering and Technology
Volume28
Issue number10
DOIs
Publication statusPublished - Oct 2005

ASJC Scopus subject areas

  • Polymers and Plastics
  • Environmental Science(all)
  • Chemical Engineering (miscellaneous)

Fingerprint Dive into the research topics of 'Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process'. Together they form a unique fingerprint.

  • Cite this