Project description
China and Europe join forces for CO2-neutral production of polymer building blocks
Olefins are a class of industrially important hydrocarbons, including ethylene, propylene and butylene, that are widely used as chemical feedstocks to produce plastics and as chemical intermediates for polymers and specialty chemicals. Conventionally, they are produced from petroleum refining and natural gas processing. The EU-funded OPTIMAL project will significantly enhance the sustainability of olefin production while strengthening Sino-European ties. The collaboration between the EU and China will harness Big Data and AI to develop technology enabling the capture and use of CO2 to produce olefins in a CO2-neutral production process.
Objective
The proposed project is to develop and maintain long term collaborations between Europe and China towards CO2 neutral Olefin production. We will realize this objective by carrying out joint research in big data and artificial intelligence (AI) for ethylene plants integrated with carbon capture and CO2 utilisation. Specifically this requires a universal set of skills such as pilot scale experimental study, process modelling and analysis, optimisation, catalysis and reaction kinetics that will be strengthened by the individual mobility of researchers between Europe and China. There are 12 partners involved in OPTIMAL with 3 industrial partners. These partners are world leading in their respective research areas. OPTIMAL is planned to start from Aug. 2021 and will continue for 65 months. There will be 28 experienced and 35 early stage researchers participating in OPTIMAL with exchange visits of 262 person months. The funding of €772,800 will be requested from European Commission to support these planned secondments. The European beneficiaries are experts at catalysis, CO2 utilisation, intensified carbon capture, reaction mechanism and kinetics & CFD studies, hybrid modelling, molecular simulation and dynamic optimisation, whilst the Chinese partners are experts at exergy analysis, process control and optimisation, solvent-based carbon capture & data-driven model development, deep reinforced learning based model free control, intelligent predictive control, physics-based reduced order model development, soft exergy sensor development and optimisation under uncertainty. Transfer of knowledge will take place through these exchange visits. We will generate at least 25 Journal publications and 25 Conference papers. 2 Special Issues will be established in leading journals such as Applied Energy. 2 Workshops and 2 Special Sessions in major international conferences will also be organised to disseminate project results.
Fields of science
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural scienceschemical sciencescatalysis
- engineering and technologyenvironmental engineeringcarbon capture engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Coordinator
S10 2TN Sheffield
United Kingdom