SubsidieMeestersPROCESSING COMPLEX MATRICES: DESCRIPTION, REACTION-SEPARATION, MODELLING
The DREAM project aims to revolutionize chemical processes by developing intensified methods for extracting and valorizing lignin from Kraft black liquor through interdisciplinary approaches and innovative modeling.
Projectdetails
Introduction
The DREAM project focuses on the transformation of complex matrices for the chemical industry of the future. It will focus on Kraft black liquor as a case study.
Proposed Processes
It proposes to design new intensified processes coupling reaction and separation in continuous mode to fractionate a complex matrix:
- Deep-eutectic solvent (DES)-assisted extraction of lignin coupled with depolymerisation.
- CO2-assisted lignin segregation coupled with pyrolysis.
- Membrane filtration coupled with oxidative depolymerisation.
Each fraction will then be valorised into products of interest through reactive distillation or catalytic upgrading.
Monitoring and Modelling
These processes will be monitored by original descriptors designed using data from on-line analysis. The modelling of each reaction and separation step will be carried out based on these descriptors, to give modules that will be assembled in a global simulation of the process.
Interdisciplinary Approach
The interdisciplinary approach combining biomass chemistry, catalysis, and chemical engineering will be enhanced through educational methods to overcome epistemological, cognitive, and communication barriers between disciplines.
Conclusion
The DREAM project represents a radically new approach for studying complex matrices, combining cutting-edge technical expertise with social and philosophical expertise to address challenges of the interdisciplinary research process. By developing new descriptors, models, and integration strategies, it aims to establish a feedstock-product relationship and design new, more efficient, and greener processes, as well as new interdisciplinary methodologies.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 3.421.471 |
| Totale projectbegroting | € 3.421.471 |
Tijdlijn
| Startdatum | 1-4-2024 |
| Einddatum | 31-3-2028 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRSpenvoerder
- Laboratorio Nacional de Energia e Geologia I.P.
- KEMIJSKI INSTITUT
- NEREUS
- UNIVERSITEIT TWENTE
- UNIVERSITE DE LORRAINE
- ECOLE SUPERIEURE DE CHIMIE PHYSIQUE ELECTRONIQUE DE LYON
Land(en)
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Dynamic Regulation of photosynthEsis in light-Acclimated organisMs
DREAM aims to enhance plant cultivation efficiency by developing innovative sensing technologies and models for optimizing photosynthesis under controlled lighting conditions.
Electrobiocatalytic cascade for bulk reduction of CO2 to CO coupled to fermentative production of high value diamine monomers
ECOMO aims to innovate sustainable production of high-value diamines from CO2 and nitrogen using bioelectrocatalysis and engineered microbes, enhancing chemical industry building blocks.
Reaction robot with intimate photocatalytic and separation functions in a 3-D network driven by artificial intelligence
CATART aims to develop autonomous reaction robots using AI and 3-D quantum dot networks to efficiently mimic natural chemical production, enhancing productivity and sustainability in the chemical industry.
Double-Active Membranes for a sustainable CO2 cycle
DAM4CO2 aims to develop innovative double active membranes for efficient CO2 capture and conversion into renewable C4+ fuels, promoting a sustainable net-zero carbon cycle.
Complex chemical reaction networks for breakthrough scalable reservoir computing
CORENET aims to develop brain-inspired computing devices using chemical reaction networks on microfluidic chips for sustainable AI applications in personalized medicine and brain-machine interfaces.
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This project aims to develop a multi-scale modeling strategy for three-phase gas-solid-liquid flows with catalysts to enhance efficiency and understanding of complex transport phenomena in industrial applications.
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This project aims to develop advanced deep learning frameworks for modeling organic and enzymatic reactions to enhance predictions of selectivity and enable sustainable synthesis.
De indamper van de toekomst
Het project ontwikkelt een modulaire ontgasser om efficiënt vloeibare (rest)stromen te verwerken, waardoor een hoog droge stofpercentage en meerwaardestromen ontstaan.