Hydrometallurgy is considered as a preferred process for battery recycling over pyrometallurgy and direct recycling due to its energy efficiency and operational flexibility. Recently, deep eutectic solvents (DESs) have emerged as promising green leaching solvents due to their biodegradability, low toxicity, and tunability. This project aims to guide the optimisation of battery recycling processes using deep eutectic solvent (DES) via data-driven approaches. The project will establish causal relationships between DES chemistry, physicochemical properties, processing conditions and metal leaching efficiency, distinguishing causal effects from simple correlations. By combining data-driven analysis, computational screening, and machine learning, the research will identify the key molecular and process descriptors governing efficient metal recovery, while also enabling the rational design of sustainable DES systems for high-efficiency battery recycling processes.
School
Chemical Engineering
Research Area
Battery recycling | Chemistry | Data science | Machine learning | Hydrometallurgy | Computational chemistry | Green chemistry | Critical minerals
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
This project will be conducted in the PartCat Laboratory at the UNSW School of Chemical Engineering, and will have access to the high-performance computing facilities. The project will be jointly supervised by Prof Rose Amal and Dr Jodie Yuwono. Students will work closely with members of the PartCat research group and collaborators with other universities.
- Gaining hands-on experience in coding, data collection and analysis
- Proposing ideal parameters for leaching process and metal extraction
- Developing collaboration with researchers from UNSW and external partners
- Continuing the research as an Honours thesis project is possible
