The effect of temperature on bubble size in flotation
Bibliographic record
Abstract
Flotation is a widely utilized mineral separation technique that is used to process large tonnages of low-grade and complex ores, which would otherwise be deemed uneconomical to exploit.This process exploits the differences in the hydrophobicity and bubble-particle interaction of the surfaces of the ore to achieve the separation of valuable minerals.This mainly surface-area-driven mechanism plays an essential role in flotation kinetics, as it drives the recovery function of the entire flotation process.The inclusion of bubble size measurements is important in modelling flotation cells as bubbles and bubble size are collectively an integral part of the flotation process.Due to seasonal temperature variations, temperature changes have been shown to affect overall flotation performance in many flotation plants located in temperate regions.This work involved finding a suitable methodology to determine the effect on the Sauter mean bubble diameter or bubble size distribution in a Denver mechanical cell by varying the temperature of the pulp at different setpoints between a range of 5 °C and 40 °C.The methodology involved two processes whereby the bubble sizes were varied by altering temperature in a Denver mechanical flotation cell.The bubble sizes were captured and analyzed using the McGill Bubble viewer and an inbuilt McGill Bubble size Analyzer software respectively, to determine the bubbles' Sauter mean diameter (D32).For the constituent of the pulp, two different frothers (MIBC and PPG 425) were selected to investigate their individual effects on the bubble size distribution under these temperature variations.The ranges specified for all the parameters were representative of industrial practice.The pulp primarily consisted of a frother solution (liquid-air phase system).The results showed that the CCC95 values for both frothers were within the range of 10-15 ppm and 5-9 ppm for MIBC and PPG 425 respectively.The CCC95 was found to decrease after 1cm/s for both frothers.Furthermore, it was observed that the CCC95 changes with temperature.Temperature was shown to affect the bubble sizes.Specifically, above 10 °C, the differences in bubble sizes of both frothers were marginal due to the frother reaching its saturation at CCC95.
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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 | 0.002 |
| 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 | 0.001 |
| 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".