NTNU – Database integration and modelling 

The Department of Materials Science and Engineering at NTNU supports the work of other partners with data analysis and modelling. In the first phase of the project, their contributions are focused on the leaching of black masses derived from the pre-treatment of Li-ion batteries. Integration of data from the full process chain is planned in later stages of the project.

For operating future recycling plants and meeting target criteria of the desired battery-grade precursors for the next generation electrode materials, a systematic integration of process data is required. The recycling of Li-ion batteries needs to address a significant variability of the feed stocks as different battery types rely on different chemical compositions. Those will include cobalt oxides (LCO, LiCoO2), lithium nickel manganese cobalt (NMC, LiNixMnyCozO2), lithium aluminium nickel cobalt (NCA), or lithium iron phosphate (LFP, LiFePO4). The optimal treatment in different steps of the recycling process depends on the chemical composition of the batteries and present impurities. NTNU will contribute by integrating data from lab trials and pilot trials of the project partners to generate databases for a systematic analysis of ideal process conditions depending on the feed stocks. These can then be used to target battery-grade precursor specifications and thereby optimize the process.

In the first phase of the project (ongoing), NTNU focuses on the hydrometallurgical leaching step, integrating lab trial data by Eramet and Chimie ParisTech. These trials aim at systematically testing different process parameters for optimal leaching rates in different Li-ion black masses. In parallel to analysing the trial results, NTNU also works on a METSIM implementation to model the thermokinetic dynamics for data-driven leaching simulations.

In the second phase (planned for 2021), NTNU will receive process data from project partners to generate databases covering the full process chain. These can then be used to develop a process step model, and systematically analyse the efficacy of process variations and process parameters for optimal recycling conditions of Li-ion batteries.