Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/100938
Type: Artigo de periódico
Title: Hybrid Neural Model For The Optimization Of Fed-batch Fermentations
Author: Costa A.C.
Henriques A.S.W.
Alves T.L.M.
Maciel Filho R.
Lima E.L.
Abstract: In this work a hybrid neural modelling methodology, which combines mass balance equations with functional link networks (FLNs), used to represent kinetic rates, is developed for bioprocesses. The simple structure of the FLNs allows the easy and rapid estimation of network weights and, consequently, the use of the hybrid model in an adaptive form. As the proposed model is able to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. The proposed methodology is used to model the processes for penicillin and ethanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example.
Editor: ABEQ, Sao Paulo, Brazil
Rights: aberto
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0033099895&partnerID=40&md5=480c13465666db8e84102e125c07541b
Date Issue: 1999
Appears in Collections:Unicamp - Artigos e Outros Documentos

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