Developing critical coalescence concentration curves using dilution and determining frother-like properties of oil sands process water
Bibliographic record
Abstract
In flotation, the rate with which mineral particles are recovered is governed by the bubbles generated. The smaller the bubbles, the more surface area is available for transport to the froth zone. Surface-active species, known as frothers, are commonly added to help produce small bubbles in flotation. They are believed to act by coalescence prevention and have different characteristics based on their chemical and structural formulas. Many methods have been developed to categorize the classes of frothers, describing different behaviours and material constants. One such method is the critical coalescence concentration (CCC) of a frother which is determined from a plot of Sauter mean bubble size (D32) vs. frother concentration, referred to here as the 'addition' method. Industrial flotation systems can encounter a number of naturally occurring surfactants and salts that also influence bubble size, such as during oil sands extraction. In effect there is a 'system' CCC. The thesis introduces a new dilution method to identify a system CCC. It is shown that the system CCC can be expressed as an equivalent frother concentration to provide context and a means of comparing water samples. Process water samples from the thickener overflow in Shell Albian Sands were tested. The study showed variability in the frother-equivalence of the process waters reaching at most the equivalent of 60 ppm of DF-250, a value that is much higher than the range of frother concentrations commonly employed in the minerals industry. The viability of using gas holdup to provide an estimate of process water D32 is also explored. A gas holdup to D32 correlation was established and used in developing the CCC curve of a sample, the advantage being gas holdup is an easier parameter to measure. It is concluded that the dilution and frother equivalent techniques can be used to help identify system hydrodynamic properties. A longer term ambition is to consider using gas holdup for on-line application to evaluate possible changes in process waters which may impact these hydrodynamic properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".