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

Agharafeie, Roshanak; Mendes, Jorge M.; Oliveira, Rui Manuel Freitas

http://hdl.handle.net/10362/173807

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Autores

Agharafeie, Roshanak

Mendes, Jorge M.

Oliveira, Rui Manuel Freitas

Resumo(s)

Integrating 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 knowledge

Descrição

Agharafeie, 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)