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

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

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

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