Factors driving entrainment in flotation systems and implications for bank management
Bibliographic record
Abstract
Entrainment is a non–selective form of flotation recovery which degrades the selectivity of the flotation process. Previous studies have found that a balanced recovery profile (i.e., each cell in a bank having equal recovery) yields the highest separation efficiency between two floatable minerals. Analysis of the empirical JKMRC water overflow rate model suggests that a balanced mass pull profile (i.e., equal mass distribution to each cell in bank) would minimise entrainment over a bank of flotation cells, whenever the value of b is greater than one. Simulations conducted in JKSimFloat comparing varying recovery and mass pull profiles support a balanced mass pull profile to maximise the separation efficiency between a floatable mineral and entrained gangue, and a balanced recovery profile to maximise the separation efficiency between two floatable minerals. Values of b > 1 were found for overflow rate data collected in several flotation systems. However, there was no phenomenological reasoning for this assumption. Based on data collected in industrial cells, the value of b was predicted in terms of the froth gas hold–up and the fraction of unburst bubbles that overflow a flotation cell. The value of gas hold–up in flotation froths is typically greater than 50 %, whereas α is typically less than 50 %. This finding lends support to the assumption that b is always greater than 1. It is recommended that this model be tested on other systems to examine its validity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".