Design of new microfluidic tools for liquid-liquid extraction chemical processes
This 12-month post-doc proposal is part of the PIA MiRAGe: Future Investment Plan “Microfluidic Tools for Accelerated R&D on Recycling Processes”.
The MIRAGE project aims to provide a set of micro and millifluidic tools, platforms and methods to accelerate, intensify and make more flexible R&D on new recycling processes for strategic metals, nuclear or non-nuclear, while minimizing quantities of materials used.
To do this, new microfluidic tools have been designed at CEA ISEC to perform counter-current liquid-liquid extraction operations. These tools make it possible to redefine the orders of magnitude in the importance of the physico-chemical phenomena involved.
The interest of this invention is twofold and will be the core work of this post-doc:
- Carry out extraction operations over very low times and liquid volumes.
- Transpose this invention to larger volumes.
Thus, initially this post-doc work will seek to study in more detail the capabilities of this new microfluidic device, then to transpose this new technique to larger contactors.
The work will be carried out in the ISEC facilities at the CEA, on the Marcoule site in partnership with the CNRS, Universities and the INP of Toulouse.
Uranium and plutonium oxide synthesis by Advanced Denitration in presence of Organic Additives
The preparation of (U,Pu)O2 MOx fuel (Mixed oxide fuel), is based on the formation of actinide oxide species from purified uranium and plutonium aqueous solutions and constitutes one of the key steps for spent nuclear fuel recycling. More specifically, the formation of actinide oxide solid solutions is a pivotal point for the multi-recycling process with FNR (Fast Neutron reactors). In this framework, the ADOA process (Advanced Denitration in presence of Organic Additives) represents a potential pathway to prepare actinide oxides without the usual valence adjustment step and enables to prepare mixed oxides thanks to a very good cationic homogeneity. This process is based on the formation of a polymeric gel, which allows homogeneous immobilization of the cations, which is then dehydrated and calcinated, ultimately leading to the synthesis of an actinide mixed-oxide. However, an optimization of the physico-chemical parameters is still needed for this process to meet the industrial MOx fuel fabrication requirements.
The aim of this postdoctoral research project will be to determine the optimal experimental conditions for the polymerisation, dehydration and calcination steps and evaluate the process robustness. The impact of these conditions on the actinide oxide morphology and its impurity content (especially residual carbon content), ease of process implementation and its adequacy with the requirements for the MOx fuel fabrication process will also be investigated.
The candidate must have a PhD in radiochemistry or solid state chemistry. Since the main part of this study will be based on glovebox experiments with the study of radioactive ceramic materials, skills on glovebox handling and materials characterization will be an asset to get this position. The results obtained during this study will be featured in patents and/or scientific publications, opening job possibilities in academic or industrial research and development sectors.
Optimization of energy transition scenarios through a dynamic Life Cycle Assessment approach
The modelling of the energy transition, with a projection until 2050 and adaptable to different countries or strategies, is complex in terms of LCA because it involves many parameters:
- a dozen possible energies, with evolutionary inventories of construction of electricity generation/storage infrastructure
- a difficulty to estimate the future of technologies for a given sector
- electricity generation in connexion with national consumption
- very contrasting scenarios, including more or less rapid increases in renewables and a decrease in nuclear power, offset or not by gas-fired combined cycle power plants
- a need to provide for several forms of electricity storage depending on the size of the unmanageable energy stock, with power levels depending on the storage time
- the correlation or not of storage power with the level of interconnection of European electricity networks.
The work will consist of analysing the inventories available in the Ecoinvent database linked to Simapro, modifying them according to the foreseeable technologies for the medium term, continuing modelling in Python language to include all the parameters.
The objective is to determine the best possible environmental trajectories for the French energy transition.
Modelling of uranium precipitation kinetics as a function of pH. Application to fluidized bed reactor
The Orano plant in Niger (Somaïr) precipitates its uranium concentrate in a fluidized bed reactor by adding sodium hydroxide. The concentrate obtained contains around 6% sodium which leads to converter penalties. Orano carried out tests at the end of 2019 on a fluidized bed in the laboratory to change the operating point of precipitation and preferentially form UO3 via a change in pH. To refine the management of the industrial unit, it is necessary to model the precipitation reactions of uranium. The candidate will have to propose and calibrate a competitive precipitation model for Na2U2O7 and UO3 based on the equilibrium constants and reaction kinetics, as a function of the pH within the reactor. In particular, the model should make it possible to understand the impact of pH on the distribution of the two main species identified in the concentrate: Na2U2O7 and UO3. This chemical model should serve as input to an existing physical model of the fluidized bed reactor. An extension of the model to other precipitation reagents, in particular magnesia, could also be studied.
Dynamic monitoring by light scattering of mass transfer between two phases in multiphase flows
The understanding and the modeling of recycling processes studied at CEA, require the measurement of both local and average properties of multiphase flows involved in chemical engineering devices. Moreover, as the R&D studies are generally conducted on small-scale experiments, access to these quantities is often difficult, especially considering that measurement methods should not disturb the observed system. In this context, optical methods, associated to extensive and rigorous physical simulations of light/particles interactions, are particularly relevant and, accordingly under specific developments since several years. Therefore, the DMRC/LGCI (CEA Marcoule), in collaboration with the laboratory IUSTI (CNRS and Aix-Marseille University), develops two optical interferometric techniques suitable for R&D studies: the Digital In-line Holography (DIH) and the Rainbow Refractometry (RR). Previous works have shown that DIH allows a simultaneous measurement of 3D-positions, shape and size of flowing particles, even considering astigmatic geometries, while RR gives access to the size and refractive index of each particle or of set of particles, which considering linear optics is directly linked to their composition. This study aims to go further in multiphase flows characterization with these two technics by following three main objectives: 1) propose original solutions for the characterization of materiel compositions thanks to DIH, 2) deepen inverse methods in RR to allow the study of clouds of particles with variable compositions and to take into account gradients of concentration around a sessile drop, 3) evaluate the relevance of these technics for lab on chip systems.
Post-doc: CNN neural network – managing data uncertainty in the learning database.
The aim is to develop algorithms able to take into account the uncertainty in the learning database of neural networks. The project fits into the context of the dynamic state estimation of liquid-liquid extraction and benefits of its knowledge-based simulator as well as industrial data. Indeed, the status of an industrial chemical process is accessible through operating parameters and available monitoring measures. However, the measures being inherently associated with uncertainty, it is necessary to make the data consistent with process knowledge. Therefore, the goal is to find the best data set of operational parameters (input of the knowledge-based simulator) to provide the model to estimate the real process state known through monitoring measures (output of the knowledge-based simulator). A convolutional neural network (CNN) is being developed in another postdoctoral project to solve the inverse problem to find the best input thanks to the measured output. A consistent set of operating parameters is going to be obtained and state of the process is going to be known during the dynamic regime of the liquid-liquid extraction process. This first step is to evaluate the impact of the uncertainty of operational parameters on the outputs of the knowledge-based model. This step will need to connect the knowledge-based model to URANIE, internal platform developed by CEA ISAS. This knowledge must be taken into account in the second part of the project. The uncertainty observed on the outputs should be taken into account in the learning loop to improve the estimation of the operational parameters by the CNN. The impact of these uncertainties on the CNN computed results must be assesed in order to trust the ability of the CNN to estimate the state of the process.
Through this project, we are at the heart of the thematic of digital simulation for the best control of complex systems.