Agharafeie, RoshanakMendes, Jorge M.Oliveira, Rui Manuel Freitas2024-10-212024-10-212024-09-27PURE: 101708465PURE UUID: 5a3f122d-e924-433e-a1db-53b0c7c3d917ORCID: /0000-0003-2251-3803/work/170346532http://hdl.handle.net/10362/173807Agharafeie, 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)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 knowledge1742365engconference poster