Optimising bioreactor processes with in-situ product removal using mathematical programming: A case study for propionate production
| dc.contributor.affiliation | Universidade de Santiago de Compostela. Departamento de Enxeñaría Química | gl |
| dc.contributor.affiliation | Universidade de Santiago de Compostela. Instituto Interdisciplinar de Tecnoloxías Ambientais (CRETUS) | gl |
| dc.contributor.area | Área de Enxeñaría e Arquitectura | |
| dc.contributor.author | Hauwaert, Lucas van der | |
| dc.contributor.author | Regueira López, Alberte | |
| dc.contributor.author | Selder, Ludwig | |
| dc.contributor.author | Zeng, An-Ping | |
| dc.contributor.author | Mauricio Iglesias, Miguel | |
| dc.date.accessioned | 2022-11-18T09:00:47Z | |
| dc.date.available | 2022-11-18T09:00:47Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Designing and operating bioreactors with in-situ product removal (ISPR) can be challenging, particularly in discontinuous systems, where the ISPR and substrate feeding need to be effectively scheduled. Mathematical models can help assess different scheduling regimes in the fermentation medium and provide a means to optimise the process. Focusing on a propionate production case study, a model of a co-culture batch fermentation with electrodialysis (the ISPR system), was developed. Using this model, the product yield and/or the productivity were maximised by 1) single objective optimisation maximising the product yield (0.49 gpropionate /gglucose) or productivity (0.75 gpropionate/L/h), 2) multi objective optimisation to pursue trade-off solutions between the yield and productivity and 3) a stochastic optimisation maximising the productivity robustly (0.64 gpropionate/L/h) to account for uncertainties associated to the model parameters. With this contribution it is demonstrated that, through mathematical models, ISPR can be implemented and adapted to the user's objectives | gl |
| dc.description.peerreviewed | SI | gl |
| dc.description.sponsorship | This work was supported by project ALQUIMIA (PID2019-110993RJ-I00) funded by the Agencia Estatal de Investigación Alquimia: Proyecto de I- d-i Programa Retos de la sociedad modalidad Jovenes investigadores convocatoria. A. Regueira would like to acknowledge the support of the Xunta de Galicia through a postdoctoral fellowship (ED481B-2021-012) | gl |
| dc.identifier.citation | Computers & Chemical Engineering 168 (2022) 108059 | gl |
| dc.identifier.doi | 10.1016/j.compchemeng.2022.108059 | |
| dc.identifier.essn | 0098-1354/ | |
| dc.identifier.uri | http://hdl.handle.net/10347/29445 | |
| dc.language.iso | eng | gl |
| dc.publisher | Elsevier | gl |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110993RJ-I00/ES/DISEÑO POR ORDENADOR DE BIOPROCESOS INNOVADORES PARA LA PRODUCCION SOSTENIBLE DE PRODUCTOS QUIMICOS | gl |
| dc.relation.publisherversion | https://doi.org/10.1016/j.compchemeng.2022.108059 | gl |
| dc.rights | ©2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/) | gl |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | |
| dc.rights.accessRights | open access | gl |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Kinetic modelling | gl |
| dc.subject | Product inhibition | gl |
| dc.subject | Downstream processing | gl |
| dc.subject | Design under uncertainty | gl |
| dc.subject | Biotechnology | gl |
| dc.subject | Operational design | gl |
| dc.title | Optimising bioreactor processes with in-situ product removal using mathematical programming: A case study for propionate production | gl |
| dc.type | journal article | gl |
| dc.type.hasVersion | VoR | gl |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | bc9b55d8-84d1-49d3-bdbe-1c9a9c276cf5 | |
| relation.isAuthorOfPublication | b098e7de-f49e-4335-9f8d-d70a445f4a69 | |
| relation.isAuthorOfPublication.latestForDiscovery | bc9b55d8-84d1-49d3-bdbe-1c9a9c276cf5 |
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