Magnetic Separation and Recycling of Goethite and Calcium Sulfate in Zinc Hydrometallurgy in the Presence of Maghemite Fine Particles
Bibliographic record
Abstract
Goethite (α-FeOOH) and calcium sulfate (CaSO 4 ·2H 2 O) are the main solid constituents of the iron oxide residues in zinc hydrometallurgy by the goethite process. Impounding these residues in tailings ponds is costly to maintain and causes a great threat to the local environment. In this study, magnetic separation was applied to separating goethite from calcium sulfate with maghemite (γ-Fe 2 O 3 ) fine particles as the carrier, which were prepared by roasting −1 μm pure magnetite mineral particles. The SEM images and XRD patterns indicated the precipitation of goethite on maghemite fine particles in the goethite process, which made the goethite aggregates magnetic, while the calcium sulfate formed nonmagnetic bulk precipitates. The magnetic goethite–maghemite aggregates were then separated effectively from calcium sulfate precipitates using a magnetic drum separator. The recovery of Fe and Ca to their corresponding products was 93.2% and 91.9%, respectively. The removal of S and As from goethite precipitates was studied by roasting with coal powder. Under the optimum conditions of the coke powder to the residue mass ratio of 4% and 1100 °C, 99.3% S and 99.5% As were removed while the goethite precipitates reached the standard of ironmaking raw materials. After drying, the calcium sulfate precipitates are used to produce cement and building materials.
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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".