Fe(III) Precipitation and Copper Loss from Sulphate-Chloride Solutions at 150 °C: A Statistical Approach
Bibliographic record
Abstract
The purification of hydrometallurgical process solutions by Fe(III) precipitation is a common and large-scale industrial operation. This step is notorious for valuable metal loss occurring with the iron precipitation product, which is usually directed to tailings. In this study, factors affecting Fe(III) precipitation and associated copper loss were studied in synthetic process solutions using statistical methods. The variables studied were: Initial acid concentration, retention time, seed addition, and initial Fe(III), Cu(II), and chloride concentrations. The importance of each variable and its interaction effects were studied against two responses, i.e., percent of Fe(III) precipitated as hematite and percent of Cu lost to solids. The results showed that a combination of high acid and moderate seeding was required to simultaneously achieve high proportions of Fe(III) precipitated as hematite and lower copper loss to the precipitates. High acid concentrations create low supersaturation for Fe(III), which minimizes the consequences of homogeneous nucleation and favors particle growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".