Hybrid Neural Network Model of an Industrial Ethanol Fermentation Process Considering the Effect of Temperature

In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These netw...

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Bibliographic Details
Published in:Applied biochemistry and biotechnology 2007-04, Vol.137-140 (1-12), p.817-833
Main Authors: MANTOVANELLI, Ivana C. C, CCOPA RIVERA, Elmer, DA COSTA, Aline C, MACIEL, Rubens
Format: Article
Language:English
Subjects:
Alcohol
Biological and medical sciences
Bioreactors
Biotechnology
Computer Simulation
Energy balance
Ethanol
Ethanol - metabolism
Fermentation
Fundamental and applied biological sciences. Psychology
Glucose - metabolism
Kinetics
Mathematical models
Methods. Procedures. Technologies
Models, Biological
Neural networks
Neural Networks (Computer)
Saccharomyces cerevisiae - metabolism
Studies
Temperature
Temperature effects
Various methods and equipments
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Summary:In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
ISSN:0273-2289
1559-0291
DOI:10.1007/s12010-007-9100-0