Reliable calibration and validation of phenomenological and hybrid models of high-cell-density fed-batch cultures subject to metabolic overflow

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dc.catalogadorvzp
dc.contributor.authorIbáñez Espinel, Francisco
dc.contributor.authorPuentes Cantor, Hernán Felipe
dc.contributor.authorBarzaga Martell, Lisbel
dc.contributor.authorSaa Higuera, Pedro
dc.contributor.authorAgosin Trumper, Eduardo
dc.contributor.authorPerez Correa, José Ricardo
dc.date.accessioned2024-05-08T17:20:57Z
dc.date.available2024-05-08T17:20:57Z
dc.date.issued2024
dc.description.abstractFed-batch cultures are the preferred operation mode for industrial bioprocesses requiring high cellular densities. Avoids accumulation of major fermentation by-products due to metabolic overflow, increasing process productivity. Reproducible operation at high cell densities is challenging (> 100 gDCW/L), which has precluded rigorous model evaluation. Here, we evaluated three phenomenological models and proposed a novel hybrid model including a neural network. For this task, we generated highly reproducible fedbatch datasets of a recombinant yeast growing under oxidative, oxygen-limited, and respiro-fermentative metabolic regimes. The models were reliably calibrated using a systematic workflow based on pre-and post-regression diagnostics. Compared to the best-performing phenomenological model, the hybrid model substantially improved performance by 3.6- and 1.7-fold in the training and test data, respectively. This study illustrates how hybrid modeling approaches can advance our description of complex bioprocesses that could support more efficient operation strategies
dc.format.extent16 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.compchemeng.2024.108706
dc.identifier.urihttps://doi.org/10.1016/j.compchemeng.2024.108706
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85511
dc.information.autorucEscuela de Ingeniería; Ibañez Espinel Francisco; S/I; 1071066
dc.information.autorucEscuela de Ingeniería; Barzaga Martell Lisbel; S/I; 1161607
dc.information.autorucEscuela de Ingeniería; Saa Higuera Pedro; 0000-0002-1659-9041; 162204
dc.information.autorucEscuela de Ingeniería; Agosin Trumper Eduardo; 0000-0003-1656-150X; 99630
dc.information.autorucEscuela de Ingeniería; Perez Correa Jose Ricardo; 0000-0002-1278-7782; 100130
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final16
dc.pagina.inicio1
dc.revistaComputers and Chemical Engineering
dc.rightsacceso restringido
dc.subjectHybrid models
dc.subjectDynamic optimization
dc.subjectHigh-density cultures
dc.subjectOverflow metabolism
dc.subjectFed-batch fermentation
dc.subjectPhysics-informed neural networks
dc.subject.ddc510
dc.subject.ddc620
dc.subject.deweyMatemática física y químicaes_ES
dc.titleReliable calibration and validation of phenomenological and hybrid models of high-cell-density fed-batch cultures subject to metabolic overflow
dc.typeartículo
sipa.codpersvinculados1071066
sipa.codpersvinculados1161607
sipa.codpersvinculados162204
sipa.codpersvinculados99630
sipa.codpersvinculados100130
sipa.trazabilidadORCID;2024-05-06
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