Steady-state simulations and commissioning to improve gold and silver recovery in a grinding and flash flotation circuit
Bibliographic record
Abstract
In "La Plata" mine (located in Zacatecas, Mexico), low gold recovery (21±5%) in the flash flotation cell was identified.This also has the consequence that, even when including the flash cell and the complete flotation circuit (rougher, scavenger, and cleaner cells), the global gold recovery does not exceed 78±5%.In the present work, an evaluation of the concentration process efficiency in the "La Plata" was carried out.This was done by process sampling, ore characterization, and steady-state simulation.For the adequate determination of the mass balance and the model´s adjustment, an appropriate sampling strategy was proposed to get representative information about the process.This was particularly difficult in plants that do not have automatic control systems.Additionally, a repetition algorithm was developed to evaluate various operating conditions through steady-state simulation.Mineralogical characterization shows that quartz, feldspar, clay, silicates, and pyrite are the most common ores in fresh feed.Gold and silver appear as electrum, native gold, and acanthite (Ag2S).These valuable minerals are liberated below 100 microns.After the sampling and simulation work (where a regrinding circuit was evaluated), modifications were made to the process and the best simulation results were commissioned.Daily monitoring was carried out for 41 days of operation, evaluating variables such as grade and recovery for gold and silver of the entire process and specifically of the flash flotation cell.The increase of gold recovery in the flash cell was 27% and the global gold recovery was close to 12%.Also, silver recovery was improved (5.5% in flash cell and 6% in global silver recovery).
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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) | 0.000 | 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 | 0.001 | 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".