Controlled oxidative precipitation of manganese from an industrial zinc sulphate solution using a sulphur dioxide and oxygen gas mixture
Bibliographic record
Abstract
The purpose of the present work was to remove selectively manganese from a neutral leach zinc-rich solution at 80ºC using the gas mixture of sulphur dioxide (SO2) and oxygen (O2) as oxidizing agent. In order to determine the optimum conditions for manganese removal using SO 2/O2, several semi-batch experiments were performed, where the effects of pH, ORP, SO2/O2 ratio, mixing intensity, etc. were investigated. Results of these tests showed that SO2/O 2 was a fast and effective oxidant for removing manganese down to ppm level provided that the appropriate reactor design, agitation and SO 2/O2 ratio were employed. In an attempt to improve the precipitate's characteristics, e.g. crystallinity and solid/liquid separation, a new technique called Step-Wise Oxidative Precipitation (S.W.O.P) was investigated using a two-reactor continuous circuit employing pH and ORP control and precipitate recycling. These tests revealed that a birnessite-like phase with general formula (Na0.7Ca0.3)Mn7O14·2.8H 2O was produced with co-precipitation of a significant amount of zinc apparently via substitution. The applied technique (S.W.O.P combined with recycling) proved effective in producing dense particles but not on lowering zinc losses. The biggest advantage of this novel oxidation technique was the total elimination of scaling. (Abstract shortened by UMI.)
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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".