Effects of Operational Parameters on the Low Contaminant Jarosite Precipitation Process-an Industrial Scale Study
Bibliographic record
Abstract
Jarosite precipitation process (JPP) is the most frequently used procedure for iron removal in the hydrometallurgical zinc extraction process. However, there is a gap in the knowledge of the relationship between operational parameters and the low contaminant JPP on the industrial scale. This study will address these issues by investigating the behavior of zinc calcine (ZC) as a neutralizing agent, exploring the source of zinc and iron through leaching experiments, and simulating the Jarosite process of the Bafgh Zinc Smelting Company (BZSC). The results showed that the zinc dissolution efficiency was 90.3% at 90 °C, and 73% of the iron present in the calcine can be solubilized. The main outcome was the iron removal of about 85% by alkaline ions present in ZC without the addition of any precipitating agent. The second target was to evaluate the effect of operational parameters on jarosite precipitation. Results revealed that increasing the temperature to 90 °C and the stirring rate to 500 RPM as well as adjusting the ZC's pH during the jarosite precipitation remarkably improved iron removal. Considering all these factors in the plant could improve Fe precipitation to around 80% on average.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".