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.fechaingreso.objetodigital2024-08-30
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.identifier.wosidWOS:001238497500001
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.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
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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