Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/197584
Type: Artigo de periódico
Title: Hybrid Neural Network Model Of An Industrial Ethanol Fermentation Process Considering The Effect Of Temperature.
Author: Mantovanelli, Ivana C C
Rivera, Elmer Ccopa
da Costa, Aline C
Maciel Filho, Rubens
Abstract: 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.
Subject: Computer Simulation
Ethanol
Fermentation
Glucose
Models, Biological
Neural Networks (computer)
Saccharomyces Cerevisiae
Temperature
Rights: fechado
Identifier DOI: 10.1007/s12010-007-9100-0
Address: http://www.ncbi.nlm.nih.gov/pubmed/18478437
Date Issue: 2007
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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