Economic Feasibility of a Mechanical Separation Process for Recycling Alkaline Batteries
Bibliographic record
Abstract
Spent primary alkaline batteries present an unused source of secondary metals in Europe and the US, with at least 300,000 metric tons of batteries being landfilled each year. While battery recycling programs exist, current hydrometallurgical and pyrometallurgical processes are not profitable when used for dedicated alkaline battery recycling, so industry growth is difficult. A novel mechanical separation process consisting of shredding, baking, magnetic separation, and specific gravity separation was developed to recycle one metric ton per hour of alkaline batteries at lower cost than current methods, while being environmentally beneficial. Financial analysis was conducted using a Process-Based Cost Model to address the challenges of modeling a recycling process. At full capacity, the cost to recycle alkaline batteries via the developed process is $529 per metric ton, +/- 25%, not including transportation, with revenue of $383 per metric ton. This cost is lower than that of other reported processes, but is still not economically feasible. With supplemental revenue of $0.3 per kg, which could come from various sources, the return on investment can occur in just under 3 years. The low value of alkaline battery recovery material is identified as the most significant economic barrier for the recycling.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".