Data‐driven flotation reagent changing evaluation via union distribution analysis of bubble size and shape
Bibliographic record
Abstract
Abstract Due to the frequent fluctuation of the ore grade and other random disturbances in the flotation process, reagents need to be changed in real time to achieve desired metallurgical parameters. To avoid erratic operation, a data‐driven reagent evaluation model was proposed. It is insensitive for bubble size distribution to differentiate froth surface images with a similar average bubble size but different grade. Since bubble shape is heavily influenced by froth grade, and bubble deformation is positively correlated to the froth grade in industry, a novel froth image feature named the union distribution of bubble size and shape was developed. Then, a multi‐output least square support vector regressor was introduced to develop a dynamic causal model to simulate the relationship between the reagents and the produced future froth surface appearance feature. Next, the health status of the reagents is rated by recognizing the category of predicted future froth. Under guidance from this reagent evaluation model, the rationality of reagent dosages can be obtained in priority. The proposed method was applied in a gold‐antimony flotation plant located in Hunan, China. It improved the efficiency of froth flotation and reduced false reagent operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".