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AuthorMjalli, F. S.
Available date2015-11-05T10:21:00Z
Publication Date2006
Publication NameChemical Engineering & Technology
ResourceWiley Online library
CitationMjalli, F. S. (2006), Adaptive and Predictive Control of Liquid-Liquid Extractors Using Neural-Based Instantaneous Linearization Technique. Chem. Eng. Technol., 29: 539–549.
ISSN1521-4125
URIhttp://dx.doi.org/10.1002/ceat.200500271
URIhttp://hdl.handle.net/10576/3711
AbstractNonlinearity of the extraction process is addressed via the application of instantaneous linearization to control the extract and raffinate concentrations. Two feed-forward neural networks with delayed inputs and outputs were trained and validated to capture the dynamics of the extraction process. These nonlinear models were then adopted in an instantaneous linearization algorithm into two control algorithms. The self-tuning adaptive control strategy was compared to an approximate model predictive control in terms of set point tracking capability, efficiency and stability. For the case of large, abrupt set point changes, the performance of the self-tuning algorithm was poor, especially for the raffinate control. The approximate model predictive control strategy was superior to the self-tuning control in terms of its ability to force the output to following the set point trajectory efficiently with smooth controller moves.
Languageen
PublisherWILEY-VCH Verlag GmbH & Co.
SubjectExtraction
Modeling
Neural networks
Process control
TitleAdaptive and Predictive Control of Liquid-Liquid Extractors Using Neural-Based Instantaneous Linearization Technique
TypeArticle
Issue Number5
Volume Number29
dc.accessType Abstract Only


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