The development of statistical <scp>ToF</scp> ‐ <scp>SIMS</scp> applied to minerals recovery by froth flotation
Bibliographic record
Abstract
The role of surface chemistry in recovery of minerals is central to several processes such as froth flotation, leaching, and electrostatic separation. In separation of base metals (eg, Cu, Pb, Zn, and Ni) by froth flotation of their minerals, usually sulphides, the attachment of these mineral particles, after hydrophobic collector addition, to air bubbles is used in operation. The stability of this bubble/particle attachment in both pulp and froth phases is dependent on the hydrophobic/hydrophilic ratio of surface species on individual mineral particle surfaces. The surfaces of individual mineral particles are a complex, distinctly non‐uniform array of hydrophobic collector molecules and hydrophilic species (eg, oxidation products, adsorbed ions, fine particles, and precipitates). Hence, this ratio varies widely between different particles of the same mineral. It has been shown to determine whether particles report, correctly or incorrectly, to concentrate or tail (residue). To improve poor flotation recovery or grade, the analysis needed is the variation of this ratio by particle and as a statistical distribution between different mineral phases across a flotation circuit (eg, feed, successive concentrates, and tails). This requires surface analysis of a large number of particles with high spatial resolution and chemical speciation. In this Surface Science Western special issue article, methods to achieve this, using time‐of‐flight secondary ion mass spectrometry and principal component analysis, developed between the Ian Wark Research Institute and Surface Science Western over 25 years are reviewed with applications to flotation. They are equally applicable to interferences in leaching, extraction, and electrostatic separation processes. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".