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AuthorMjalli, F.?S.
AuthorAl-Asheh, S.
Available date2015-11-05T13:23:46Z
Publication Date2005
Publication NameChemical Engineering & Technology
ResourceWiley Online Library
CitationMjalli, F.?S. and Al-Asheh, S. (2005), Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process. Chemical Engineering & Technology, 28: 1191�1200.
ISSN1521-4125
URIhttp://dx.doi.org/10.1002/ceat.200500166
URIhttp://hdl.handle.net/10576/3754
AbstractIn this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.
Languageen
PublisherWILEY-VCH Verlag GmbH & Co.
SubjectBioreactors
Fermentation
Modeling
Prediction
TitleNeural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process
TypeArticle
Issue Number10
Volume Number28


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