Mapping and modelling of Direct Lithium Extraction (DLE) unit operations with a view to their surrogate modelling

IFP Energies nouvelles (IFPEN) est un acteur majeur de la recherche et de la formation dans les domaines de l’énergie, du transport et de l’environnement. De la recherche à l’industrie, l’innovation technologique est au cœur de son action, articulée autour de quatre priorités stratégiques : Mobilité Durable, Energies Nouvelles, Climat / Environnement / Economie circulaire et Hydrocarbures Responsables.

Dans le cadre de la mission d’intérêt général confiée par les pouvoirs publics, IFPEN concentre ses efforts sur :

• l’apport de solutions aux défis sociétaux de l’énergie et du climat, en favorisant la transition vers une mobilité durable et l’émergence d’un mix énergétique plus diversifié ;
• la création de richesse et d’emplois, en soutenant l’activité économique française et européenne et la compétitivité des filières industrielles associées.

Partie intégrante d’IFPEN, l’école d’ingénieurs IFP School prépare les générations futures à relever ces défis.

Mapping and modelling of Direct Lithium Extraction (DLE) unit operations with a view to their surrogate modelling

Demand for battery-grade lithium is growing steadily, while conventional extraction routes are reaching their limits (land footprint, water consumption, long production cycles). Direct Lithium Extraction (DLE) technologies — sorption, ion exchange, solvent extraction, membrane and electrochemical processes — offer a credible alternative for dilute brines (geothermal brines, produced waters, salar brines). Yet they are almost always assessed in isolation, whereas the industrial viability of a DLE process is decided at the scale of the complete chain, adapted to the chemistry of each brine.

Description

For a given resource, the flowsheet configuration can profoundly change the level and structure of costs. The optimal design of a DLE process therefore cannot result from a single unit operation considered in isolation, but from the consistent integration of pretreatment, extraction and refining steps. Evaluating many flowsheet scenarios in this way calls for fast models of each process block, derived from reference physical models.

The internship pursues two complementary objectives: mapping the unit operations of DLE chains according to feed characteristics and target product (carbonate or hydroxide) and reviewing — or developing where missing — the physical models of each unit operation in a form suited to optimisation.

The work will be divided into the following tasks:

• Review the literature, industrial reports and feasibility studies: existing DLE chains, influence parameters (lithium content, contaminants, salinity, temperature) and cost structures
• Build a taxonomy of unit operations by feed profile and target product, making explicit the activation conditions of each block (pretreatment, extraction, purification, concentration, conversion)
• Review, develop or adapt the physical models of each unit operation (column sorption, membrane transport, precipitation and crystallisation) and validate them with literature or supplier data
• Keep their computational cost compatible with a later surrogate-modelling phase

Required profile

Final-year engineering or master’s student in process engineering, chemical engineering or applied mathematics, with a strong interest in process modelling and scientific computing.

• Python or MATLAB skills expected; exposure to numerical optimisation or surrogate modelling appreciated but not required.

Additional information
- Duration of the internship : 6 months
- Workplace : IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
- Transport : public transportation / personal vehicle
- Paid internship 1150€/month (gross)