HydroFloat™ Flotation of Fine Copper Tailings: Performance Analysis, Hydrodynamics, and Reagent Optimization
Bibliographic record
Abstract
HydroFloat™ is a fluidised-bed flotation technology developed to recover coarse particles typically lost in conventional flotation.While its performance is well-documented for coarse, well-liberated feeds, its application to finer, poorly liberated tailings remains poorly understood, particularly regarding the role of collector chemistry.This study investigates the HydroFloat™ flotation of deslimed copper tailings (+53 µm), characterised by poor mineral liberation and complex copper-gangue intergrowths.Three reagent schemes were evaluated: a conventional thiol collector (potassium amyl xanthate, PAX), a safer xanthate replacement (INTERCOL® C4450), and a blend of INTERCOL® C4450 with diesel.Results show that stable fluidisation and meaningful copper recovery (>60%) can be achieved even at these finer sizes using controlled teeter water flow (2-3 L/min).PAX provided the highest copper recovery but with poor selectivity, while INTERCOL® C4450 improved grade at the expense of recovery.The diesel-enhanced C4450 blend offered a performance compromise, improving grade-recovery balance.These findings demonstrate that careful collector selection can partially offset mineralogical limitations in fluidised-bed flotation and broaden HydroFloat™ applicability to finer tailings.Future work will focus on reagent optimization and linking fluidisation hydrodynamics to metallurgical performance.
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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) | 0.000 | 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".