A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS

Detalhes bibliográficos
Autor(a) principal: da Cruz,A.J.G.
Data de Publicação: 1997
Outros Autores: Hokka,C.O., Giordano,R.C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Chemical Engineering
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321997000400004
Resumo: The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data
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spelling A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKSBioprocessneural networksmodellingThe production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new dataBrazilian Society of Chemical Engineering1997-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321997000400004Brazilian Journal of Chemical Engineering v.14 n.4 1997reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66321997000400004info:eu-repo/semantics/openAccessda Cruz,A.J.G.Hokka,C.O.Giordano,R.C.eng1998-10-06T00:00:00Zoai:scielo:S0104-66321997000400004Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:1998-10-06T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false
dc.title.none.fl_str_mv A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
title A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
spellingShingle A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
da Cruz,A.J.G.
Bioprocess
neural networks
modelling
title_short A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
title_full A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
title_fullStr A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
title_full_unstemmed A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
title_sort A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
author da Cruz,A.J.G.
author_facet da Cruz,A.J.G.
Hokka,C.O.
Giordano,R.C.
author_role author
author2 Hokka,C.O.
Giordano,R.C.
author2_role author
author
dc.contributor.author.fl_str_mv da Cruz,A.J.G.
Hokka,C.O.
Giordano,R.C.
dc.subject.por.fl_str_mv Bioprocess
neural networks
modelling
topic Bioprocess
neural networks
modelling
description The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data
publishDate 1997
dc.date.none.fl_str_mv 1997-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321997000400004
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66321997000400004
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0104-66321997000400004
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Brazilian Society of Chemical Engineering
publisher.none.fl_str_mv Brazilian Society of Chemical Engineering
dc.source.none.fl_str_mv Brazilian Journal of Chemical Engineering v.14 n.4 1997
reponame:Brazilian Journal of Chemical Engineering
instname:Associação Brasileira de Engenharia Química (ABEQ)
instacron:ABEQ
instname_str Associação Brasileira de Engenharia Química (ABEQ)
instacron_str ABEQ
institution ABEQ
reponame_str Brazilian Journal of Chemical Engineering
collection Brazilian Journal of Chemical Engineering
repository.name.fl_str_mv Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)
repository.mail.fl_str_mv rgiudici@usp.br||rgiudici@usp.br
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