The Renewable Energy Carriers: Experimental and Statistical Approaches; Process Optimization team covers:
- the production of fluid and solid renewable energy carriers by thermochemical conversion from biomass, agricultural and industrial by-products, and industrial waste.
- the control of multi-energy networks with storage to compensate for the intermittency of renewable resources
- the modeling of thermal and thermochemical processes (thermal storage, concentrated solar energy, pyrolysis, gasification, etc.).
Research objectives
The aim of our research is to develop and propose innovative methods combining experimental studies and approaches involving multi-physics and multi-scale modeling to improve the thermochemical conversion of lignocellulosic biomass or co-products (household waste, industrial waste, etc.).
The unit operations studied are mainly pyrolysis and gasification, with an experimental approach ranging from particle to pilot scale. A distinctive feature of our work is the detailed consideration of the chemistry of biomass degradation (mechanics, transfer and chemical kinetics). The numerical approaches developed are mainly based on Monte Carlo methods for modeling heat transfer, chemical kinetics and solar resources, and on deep learning methods for sizing and controlling multi-energy networks involving renewable energy and thermal storage.
Scientific challenges
Several challenges need to be addressed:
- How can we improve the energy and environmental performance of the processes?
- What experimental and numerical methods need to be developed and/or adopted to describe the chemical kinetics during thermochemical conversion of complex biomass and biowaste media?
- How can innovative statistical approaches be used in multi-physical and multi-scale modeling?
- How to couple knowledge and statistical models based on experimental data?
Members:
M. CARRIER, J.L. DIRION, S. EIBNER, M. EL HAFI, F.J. ESCUDERO, M. MILHÉ, S. SALVADOR