Towards Using MMO Anodes in Zinc Electrorefining: Mn Removal by Simulated Plant Off-Gas
Bibliographic record
Abstract
Implementing mixed metal oxide (MMO) anodes in zinc electrowinning is highly desired due to the considerable reduction in electrical energy consumption. However, the presence of manganese in the electrolyte is a major obstacle for implementing MMO anodes in the zinc cell houses. In this work, we explore the possibility of using plant off-gas, containing SO2, to remove manganese. A SO2/air gas mixture with different SO2 and O2 concentrations was therefore used for the oxidative precipitation of manganese. It was shown that the manganese oxidation reaction is highly pH-dependent. Calcium hydroxide was used to control the pH during the process. Different operating parameters, i.e., pH, SO2/air ratio, reaction time, and effect of cobalt as a reaction catalyst, were investigated. Optimal conditions for manganese removal were reported. Under the optimal conditions, the manganese concentration decreased from 1 g L−1 to less than 1 mg L−1 within 30 min. Precipitates were characterized using EDS, XRF, and XPS techniques and showed coprecipitation of manganese, zinc, gypsum, and cobalt.
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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 | 0.000 |
| 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 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".