Application of Industrial-Scale Lithium Sulphate Electrolysis in Battery Recycling
Bibliographic record
Abstract
As the world charges towards electrification and sustainable transportation, it is critical that the entire supply chain is equally sustainable. Industrial processes must be tailored towards circular processes in which emissions and effluents to the environment are minimized, if not eliminated altogether. When it comes to the production of battery grade lithium hydroxide monohydrate, a critical component of lithium ion batteries (LIBs), and the recovery of the lithium in spent LIBs. NORAM Electrolysis Systems Inc (NESI) has developed electrochemical technologies in which effluents are greatly reduced. NESI has developed a flexible, multi-compartment industrial electrolyser for salt splitting (NORSCAND®) applications including electrolysis of lithium sulphate to produce battery-grade lithium hydroxide (LHM). This electrolysis step can be used for the initial production of LHM or for recovering the LHM from recycling LIBs. In either case sulphuric acid is a by-product of the electrolysis process and it can then be recycled for dissolution of black mass in battery recycling processes or for e.g. SX regeneration. In both cases the incorporation of electrolysis in the flowsheet eliminates effluent streams. This presentation will outline the product quality and environmental benefits of electrolysis applied as part of a battery recycling flowsheet. It will outline the approach to electrolyser scale-up and present test and performance data from industrial-scale application. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".