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AuthorMjalli, Farouq
Available date2010-01-07T10:21:06Z
Publication Date2004-07-30
Publication NameChemical Engineering Science
Identifierhttp://dx.doi.org/10.1016/j.ces.2004.07.117
CitationFarouq 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
URIhttp://hdl.handle.net/10576/10637
AbstractThe 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.
Languageen
PublisherElsevier Ltd
SubjectNeural networks
SubjectModel predictive control
SubjectModeling
SubjectDynamic simulation
SubjectLiquid–liquid extraction
SubjectScheibel column
TitleNeural network model-based predictive control of liquid–liquid extraction contactors
TypeArticle


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