Logo do repositório
 
Publicação

Deep Hybrid Modelling of a Supercritical CO2 Extraction Process [poster]

2024-09-27Póster em conferência Acesso aberto
Ver registo simples
dc.contributor.authorAgharafeie, Roshanak
dc.contributor.authorMendes, Jorge M.
dc.contributor.authorOliveira, Rui Manuel Freitas
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.institutionDQ - Departamento de Química
dc.date.accessioned2024-10-21T22:18:05Z
dc.date.available2024-10-21T22:18:05Z
dc.date.issued2024-09-27
dc.descriptionAgharafeie, R., Mendes, J. M., & Oliveira, R. M. F. (2024). Deep Hybrid Modelling of a Supercritical CO2 Extraction Process [poster]. Poster session presented at Data Research Meetup by MagIC, Lisbon, Portugal. --- This work was supported by the Associate Laboratory for Green Chemistry—LAQV, which is financed by national funds from FCT/MCTES (LA/P/0008/2020, UIDB/50006/2020 and UIDP/50006/2020). This work received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101099487—BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI:10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS)
dc.description.abstractIntegrating deep learning and big data has the potential to significantly enhance efficiency in biomanufacturing. However, the industry currently faces a challenge due to inadequate big data infrastructure. A promising solution to this issue is the development of a hybrid neural network (HNN) that combines deep neural networks (DNN) with existing process knowledgeen
dc.description.versionpublishersversion
dc.description.versionunpublished
dc.format.extent1
dc.format.extent742365
dc.identifier.otherPURE: 101708465
dc.identifier.otherPURE UUID: 5a3f122d-e924-433e-a1db-53b0c7c3d917
dc.identifier.otherORCID: /0000-0003-2251-3803/work/170346532
dc.identifier.urihttp://hdl.handle.net/10362/173807
dc.language.isoeng
dc.peerreviewedno
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationhttps://doi.org/10.54499/UIDB/04152/2020
dc.titleDeep Hybrid Modelling of a Supercritical CO2 Extraction Process [poster]en
dc.typeconference poster
degois.publication.issue1
degois.publication.titleData Research Meetup by MagIC
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardNumberUIDB/50006/2020
oaire.awardTitleInformation Management Research Center
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublicationadc84c24-ba1d-4bcd-b753-2128ce9a5faa
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Deep_Hybrid_Modelling_of_a_Supercritical_CO2_Extraction_Process.pdf
Tamanho:
724.97 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NIMS: MagIC - Documentos de conferências nacionais