Neural network model-based predictive control of liquid-liquid extraction contactors

Farouq S. Mjalli*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


The inherent complex nonlinear dynamic characteristics and time varying transients of the liquid-liquid extraction process draw the attention to the application of nonlinear control techniques. In this work, neural network-based control algorithms were applied to control the product compositions of a Scheibel agitated extractor of type I. Model predictive control algorithm was implemented to control the extractor. The extractor hydrodynamics and mass transfer behavior were modeled using the non-equilibrium backflow mixing cell model. It was found that model predictive control is capable of solving the servo control problem efficiently with minimum controller moves. This study will be followed by more work concentrated on using different neural network-based control algorithms for the control of extraction contactors.

Original languageEnglish
Pages (from-to)239-253
Number of pages15
JournalChemical Engineering Science
Issue number1
Publication statusPublished - Jan 2005
Externally publishedYes


  • Dynamic simulation
  • Liquid-liquid extraction
  • Model predictive control
  • Modeling
  • Neural networks
  • Scheibel column

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Neural network model-based predictive control of liquid-liquid extraction contactors'. Together they form a unique fingerprint.

Cite this