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Technologies focused on decarbonization of the electricity and transport sector (wind, solar, electric vehicle batteries, etc.) will mobilize a significant quantity of metals in the coming decades.
This thesis focuses on a global economic and strategic thinking of the deployment of hydrogen at the global level. It has 3 parts. First, it will study the diffusion of technologies and the deployment of renewable and low-carbon hydrogen at the global level by 2050.
The environmental impact of the public policies implemented so far in the field of transport remains mixed, with GHG emissions from this sector still representing 30% of national emissions. In addition, some of these policies are the subject of strong mistrust on the part of citizens (e.g. the movement of yellow vests).
The control of alumina support textural properties is a very important task in the framework of innovative catalytic material manufacturing. Mass transport in the porous medium is strongly affected by the micro-structure and could decrease the impregnation efficiency of the active phase and the resulting catalytic activity.
In the context of complex fluid process development (bio-process, water treatment, material synthesis, plastic recycling), a thesis is suggested in order to study and model the local shear distribution in mixing unit for Reynolds numbers in between 2000 <Re<20000.
Recycling of PET polymer through chemical recycling routes has attracted a great deal of interest in recent years, to cope with plastics streams not handled by classical industrial PET mechanical recycling routes.
Biofuels as well as bio-based chemicals production represent a major transition to reduce our dependence on fossil resources.
Numerical simulation is essential to model flow in porous media and in particular to study underground storages of CO2 or H2. It indeed allows one to test different scenarii by simulating coupled physical mechanisms stemming from various domains (geology, geochemistry, flow and soil mechanics...)
Automotive industry is moving fast to offer hybrid and full electric vehicles in order to reduce the greenhouse gas emissions and pollutants. Then electric motors may be considered as one of the most important device in the next emerging vehicle architecture.
With the reduction of hydrocarbon resources and increasingly stringent standards to deal with the problem of climate change, the development of clean-up internal combustion engines constitutes a major technical challenge. The use of hydrogen is considered a promising alternative for vehicles. Two main options are possible.
Among IFPEN applications, finding the optimal configuration of the inputs of a model leading to the best admissible performance appears in the design of complex systems such as electric motors or floating wind turbines.
Reinforcement Learning (RL) has been successfully applied to a number of problems, such as robotic control, task scheduling, and telecommunications.