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

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contributor.author Mjalli, Farouq en_US
date.accessioned 2010-01-07T10:21:06Z en_US
date.available 2010-01-07T10:21:06Z en_US
date.issued 2004-07-30 en_US
identifier.citation Farouq S. Mjalli, Neural network model-based predictive control of liquid–liquid extraction contactors, Chemical Engineering Science, Volume 60, Issue 1, January 2005, Pages 239-253 en_US
identifier.uri http://dx.doi.org/10.1016/j.ces.2004.07.117 en_US
identifier.uri http://hdl.handle.net/10576/10637 en_US
description.abstract 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. en_US
language.iso en en_US
publisher Elsevier Ltd en_US
subject Neural networks en_US
subject Model predictive control en_US
subject Modeling en_US
subject Dynamic simulation en_US
subject Liquid–liquid extraction en_US
subject Scheibel column en_US
title Neural network model-based predictive control of liquid–liquid extraction contactors en_US
type Article en_US


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