Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , , , |
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=S0104-66322005000100003 |
Resumo: | Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior. |
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Brazilian Journal of Chemical Engineering |
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Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modelingNeural networksEnzymesModelingProduct inhibitionSubstrate inhibitionCellobioseNeural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.Brazilian Society of Chemical Engineering2005-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000100003Brazilian Journal of Chemical Engineering v.22 n.1 2005reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322005000100003info:eu-repo/semantics/openAccessCorazza,F. C.Calsavara,L. P. V.Moraes,F. F.Zanin,G. M.Neitzel,I.eng2005-03-15T00:00:00Zoai:scielo:S0104-66322005000100003Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2005-03-15T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
title |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
spellingShingle |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling Corazza,F. C. Neural networks Enzymes Modeling Product inhibition Substrate inhibition Cellobiose |
title_short |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
title_full |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
title_fullStr |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
title_full_unstemmed |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
title_sort |
Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling |
author |
Corazza,F. C. |
author_facet |
Corazza,F. C. Calsavara,L. P. V. Moraes,F. F. Zanin,G. M. Neitzel,I. |
author_role |
author |
author2 |
Calsavara,L. P. V. Moraes,F. F. Zanin,G. M. Neitzel,I. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Corazza,F. C. Calsavara,L. P. V. Moraes,F. F. Zanin,G. M. Neitzel,I. |
dc.subject.por.fl_str_mv |
Neural networks Enzymes Modeling Product inhibition Substrate inhibition Cellobiose |
topic |
Neural networks Enzymes Modeling Product inhibition Substrate inhibition Cellobiose |
description |
Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-03-01 |
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=S0104-66322005000100003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322005000100003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322005000100003 |
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.22 n.1 2005 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 |
_version_ |
1754213171850117120 |