Selective Recovery of Zinc from Alkaline Batteries via a Basic Leaching Process and the Use of a Machine Learning-Based Digital Twin for Predictive Purposes
Bibliographic record
Abstract
Recycling the metals found in spent batteries offers both environmental and economic benefits, especially when extracted and purified using environmentally friendly processes. Two basic leaching agents were tested and compared: ammonium hydroxide (NH4OH) and sodium hydroxide (NaOH). Using NH4OH 4 M at 25 °C, 30.5 ± 0.7 wt. % of zinc (Zn) was dissolved for a solid/liquid (S/L) ratio of 1/10 (g of black mass (BM)/mL of solution); meanwhile, with NaOH 6 M at 70 °C, and an S/L ratio of 1/5 (g of BM/mL of solution), 69.9 ± 2.8 wt. % of the Zn initially present in the BM of alkaline batteries was leached. A virtual representation of the experimental data through digital twins of the alkaline leaching process of the BM was proposed. For this purpose, 90% of the experimental data were used for training a supervised learning procedure involving 600 different artificial neural networks (ANNs) and using up to 12 activation functions. The application was able to choose the most suitable ANN using an ANOVA analysis. After the training step, the network was tested by predicting the outputs of inputs that were not used in the training process, to avoid overfitting in a validating process with 10% of the data. The best model was employed for estimating the degree of leaching of different metals that can be obtained from BM, obtaining a data deviation of less than 10% for highly concentrated compounds such as Zn.
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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 | 0.000 | 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 | 0.001 |
| 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 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".