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dc.contributor.authorCruz, A. J. G.
dc.contributor.authorSilva, A. S.
dc.contributor.authorAraujo, M. L. G. C. [UNESP]
dc.contributor.authorGiordano, R. C.
dc.contributor.authorHokka, C. O.
dc.date.accessioned2014-05-20T14:17:31Z
dc.date.available2014-05-20T14:17:31Z
dc.date.issued1999-06-01
dc.identifierhttp://dx.doi.org/10.1590/S0104-66321999000200010
dc.identifier.citationBrazilian Journal of Chemical Engineering. Brazilian Society of Chemical Engineering, v. 16, n. 2, p. 179-183, 1999.
dc.identifier.issn0104-6632
dc.identifier.urihttp://hdl.handle.net/11449/25246
dc.description.abstractThis paper reports on the use of the gas balance and dynamic methods to obtain an estimate of the volumetric oxygen transfer coefficient (kLa) in a conventional reactor during the growth phase of the microorganism Cephalosporium acremonium. A new way of calculating kLa by the dynamic method employing an electrode with a slow response, is proposed. The calculated values of kLa were used in the training of a feedforward neural network, for which the inputs were the parameter measurements of the related variables. The neural network technique proved effective, predicting values of kLa accurately from input data not used during the training phase. In contrast, the gas balance method was shown to be less useful. This could be attributed to the poor data obtained with the apparatus used to measure the oxygen in the exhaust gas, explained by the low rate of oxygen consumption by the microorganism.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent179-183
dc.language.isoeng
dc.publisherBrazilian Society of Chemical Engineering
dc.relation.ispartofBrazilian Journal of Chemical Engineering
dc.sourceSciELO
dc.subjectneural network techniqueen
dc.subjectdynamic methodsen
dc.subjectvolumetric oxygen transfer coefficienten
dc.titleEstimation of the volumetric oxygen tranfer coefficient (KLa) from the gas balance and using a neural network techniqueen
dc.typeArtigo
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.description.affiliationUniversidade Federal de São Carlos (UFSCar)
dc.description.affiliationUNESP
dc.description.affiliationUnespUNESP
dc.identifier.doi10.1590/S0104-66321999000200010
dc.identifier.scieloS0104-66321999000200010
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-0033365912
dc.relation.ispartofjcr0.925
dc.relation.ispartofsjr0,395
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