Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/104726
Type: Artigo de evento
Title: Hybrid Neural Network Model Of An Industrial Ethanol Fermentation Process Considering The Effect Of Temperature
Author: Mantovanelli I.C.C.
Rivera E.C.
Da Costa A.C.
Maciel Filho R.
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. © 2007 Humana Press Inc.
Editor: 
Rights: fechado
Identifier DOI: 10.1007/s12010-007-9100-0
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-42949145253&partnerID=40&md5=c4e6fc1429e5d50267fae914f851545a
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

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