Flotation of zinc silicate ore: A focus on effective parameters, synergistic effect of mixed cationic collectors and its mechanism
Bibliographic record
Abstract
The present study addresses the direct flotation route for the concentration of a zinc silicate ore. Flotation of hemimorphite faces some challenges due to the similarity of hemimorphite and quartz surface properties. XRD, and XRF analyses, optical mineralogy and SEM with X-ray mapping were performed for characterization. The results showed that the quartz, hemimorphite, calcite and dolomite are the main minerals. In the flotation process, combined form of Octadecylamine and Armac T, as mixed cationic collectors were introduced. Flotation tests were employed in two steps to evaluate the individual effect of the variables such as: size distribution of feed and the presence of Armac T in the first step, alkalinity, types and concentrations of reagents (Armac T and stearyl amine (collectors), sodium sulfide (sulphidizing agent), and sodium hexametaphosphate (dispersant) in the second step. In optimal conditions, the grade and recovery of zinc in concentrate were ~33 % and 83 %, respectively. Finally, for removing the detrimental effect of fine particles, de-sliming of pulp feed accomplished which resulted in a remarkable increase in the recovery of hemimorphite concentrate (12.67%) and a decrease in the grade of zinc in tailing (6.11%). FTIR analysis showed the adherence of collectors on hemimorphite particles increased after de-sliming.
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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) | 0.001 | 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".