Hydrometallurgical Process and Economic Evaluation for Recovery of Zinc and Manganese from Spent Alkaline Batteries
Why this work is in the frame
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Bibliographic record
Abstract
An innovative, efficient, and economically viable process for the recycling of spent alkaline batteries is presented herein. The developed process allows for the selective recovery of Zn and Mn metals present in alkaline batteries. The hydrometallurgical process consists of a physical pre-treatment step for separating out the metal powder containing Zn and Mn, followed by a chemical treatment step for the recovery of these metals. Sulfuric acid was used for the first leaching process to dissolve Zn(II) and Mn(II) into the leachate. After purification, Mn was recovered in the form of MnO2, and Zn in its metal form. Furthermore, during the second sulfuric acid leaching, Na2S2O5 was added for the conversion of Mn(IV) to Mn(II) (soluble in the leachate), allowing Mn to be recovered as MnCO3. Masses of 162 kg of Zn metal and 215 kg of Mn (both in the form of MnO2 and MnCO3) were recovered from one ton of spent alkaline batteries. The direct operating costs (chemicals, labor operation, utilities, energy) and indirect costs (amortization, interest payment) required for a plant treating 8 tons of spent batteries per day was calculated to be $CAD 726 and $CAD 534 per ton, respectively, while the total revenue from the sale of the metals was calculated at $CAD 1359.6 per ton of spent batteries. The development of this type of cost-effective industrial process is necessary for a circular economy, as it contributes to addressing environment- and energy-related issues, and creates opportunities for the economic utilization of metals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it