Applied Online Bubble Size Distribution Measurement in a Pilot Flotation Cell Based on Image Analysis
Bibliographic record
Abstract
The distribution of bubble size in the pulp is a parameter directly related to the flotation kinetics, but its measurement is complex to determine due to the presence of particles and cluster of bubbles. The existing equipment for the measurement of bubble size (McGill and UCT), which operate manually and batch, requires specialized operators in image analysis. On the other hand, the McGill technique has not been directly validated with bubble swarms and only 10% of the sampled bubbles are analyzed. These aspects have limited the technology transfer and sustainability in the measurement of bubble size. To solve the problems presented, a device based on the McGill technique was designed and implemented. Furthermore, algorithms were implemented to increase the statistical significance of the measurement of bubbles per image. The validation consisted of a comparison of the degree of detection using the software manually and automatically (undetected remaining bubbles). As a result, it is possible to predict the bubble size distributions with an error of less than 5% and derivations close to 0.1 [mm] in the determination of D32, using an average of 100 images. In conclusion, the new device and algorithms improve the accuracy of BSD measurements, helping to optimize the process, predict, control flotation kinetics, and be used as a troubleshooting tool. The new device and algorithms improve the accuracy of BSD measurements, helping to optimize the process, predict, control flotation kinetics, and be used as a troubleshooting tool.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".