An optimisation study for leaching synthetic scheelite in H <sub>2</sub> SO <sub>4</sub> and H <sub>2</sub> O <sub>2</sub> solution
Bibliographic record
Abstract
Tungsten production primarily relies on scheelite, a secondary resource due to its complex ore composition and lower grade compared to high-grade wolframite. Synthetic scheelite gains significance for its low impurity content and accessibility in ongoing laboratory-based investigations. Recent advancements offer a feasible environment-friendly leaching method using a mixed solution of H2SO4 and H2O2 under normal pressure and moderate temperatures. However, comprehensive research on this novel method is lacking, emphasising the need for collaborative exploration and operational optimisation. The present work is to provide insights and improvements in reagent usage to promote economic and environmental sustainability. A detailed investigation into the thermal decomposition process was also conducted without compromising leaching efficiency. The findings suggest the potential for eco-friendly lixivium recycling with reduced levels of chemicals, decreasing operational costs. Notably, optimising the thermal decomposition duration to 6 hours at an L/S (mL/g) ratio of 10 enhances H2WO4 crystallization. Furthermore, experiments without H2SO4 supplementation highlight the system's optimisation potential. Finally, the leaching process was optimised by decreasing H2SO4 concentration to 1 mol/L from 3 mol/L, increasing the temperature to 60°C, and extending the leaching duration to 120 minutes. This leads to a cost-effective synthetic scheelite leaching process with environmental benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".