Mass balance and economic study of a treatment chain for nickel, cobalt and rare earth elements recovery from Ni-MH batteries
Bibliographic record
Abstract
The aim of this project is to develop and evaluate the economic performance of a complete process for recovering nickel, cobalt, and rare earths (REEs) from nickel metal hydride (Ni-MH) battery waste. The main elements contained in the battery powder are Ni (523 g/kg), La (58 g/kg), Co (39 g/kg), Zn (21 g/kg), Nd (19 g/kg), Sm (19 g/kg) and Ce (14 g/kg). Metal leaching was carried out with 2 M sulfuric acid, solubilising 100% of Ni, 93% of Co and 94% of REEs. Rare earths were precipitated with NaOH, then purified after resolubilization in nitric acid. Solvent extraction with bis(2-ethylhexyl) phosphoric acid (D2EHPA) followed by bis(2,4,4-trimethylpentyl) phosphinic acid (Cyanex 272) was used to separate Ni and Co. At the end of the process, REEs, nickel, and cobalt were recovered as oxides after precipitation as oxalates. The REE, nickel and cobalt oxides obtained have purities of 97.6%, 97.2% and 93.2% respectively. A techno-economic study was carried out using SuperPro Designer software. In this scenario, plant capacity was set at 1.0 t of used battery powder per hour for an operating period of 8 h/d and 250 days per year. The total investment was estimated at $26.9 million, with a payback period of 1.58 years. For a 15-year life, the net present value of this project is estimated at $95.9 million, with an interest rate of 7%. The internal rate of return is estimated at 46.1%, which is considered acceptable and economically viable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".