Please use this identifier to cite or link to this item: https://repository.ucc.edu.co/handle/20.500.12494/41369
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dc.creatorDiana C. Lopez C-
dc.creatorTilman Barz-
dc.creatorMariana Peñuela-
dc.creatorVillegas Quiceno, Adriana Patricia-
dc.date.accessioned2021-12-16T22:15:28Z-
dc.date.available2021-12-16T22:15:28Z-
dc.date.issued2013-
dc.identifierhttps://doi.org/10.1002/btpr.1753-
dc.identifier.issn87567938es
dc.identifier.urihttp://hdl.handle.net/20.500.12494/41369-
dc.description.abstractIn this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over-parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill-posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio-ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross-validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems. © 2013 American Institute of Chemical Engineers.es
dc.description.provenanceMade available in DSpace on 2021-12-16T22:15:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2013en
dc.format.extent1082-1064es
dc.publisherWiley-Blackwelles
dc.relation.ispartofBIOTECHNOL PROGRes
dc.subjectBio-ethanolses
dc.subjectIdentifiability analysises
dc.subjectIll posed problemes
dc.subjectNon-linear least squareses
dc.subjectParameter subsetses
dc.subjectSugar-cane bagassees
dc.subjectAlgorithmses
dc.subjectBioethanoles
dc.subjectFermentationes
dc.subjectInverse problemses
dc.subjectParameter estimationes
dc.subjectIterative methodses
dc.subjectalcoholes
dc.subjectalgorithmes
dc.subjectarticlees
dc.subjectbio-ethanoles
dc.subjectbiological modeles
dc.subjectbiotechnologyes
dc.subjectfermentationes
dc.subjectidentifiability analysises
dc.subjectill-posed problemes
dc.subjectmetabolismes
dc.subjectnonlinear least squares parameter estimationes
dc.subjectnonlinear systemes
dc.subjectparameter subset selectiones
dc.subjectSSF processes
dc.subjectsugarcane bagassees
dc.subjectbio-ethanoles
dc.subjectidentifiability analysises
dc.subjectill-posed problemes
dc.subjectnonlinear least squares parameter estimationes
dc.subjectparameter subset selectiones
dc.subjectSSF processes
dc.subjectsugarcane bagassees
dc.subjectAlgorithmses
dc.subjectBiotechnologyes
dc.subjectEthanoles
dc.subjectFermentationes
dc.subjectModelses
dc.subjectBiologicales
dc.subjectNonlinear Dynamicses
dc.titleModel-based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio-ethanol productiones
dc.typeArtículo-
dc.creator.mailadriana.villegas@ucc.edu.coes
dc.identifier.bibliographicCitationDC.LC,TB,MP,VILLEGAS AP. Model-based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio-ethanol production. Biotechnol Prog. 2013. 29. (4):p. 1064-1082. .es
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