Flotation processing of copper-containing technogenic raw materials using a composite flotation reagent
Bibliographic record
Abstract
The article presents the results of laboratory research on the possibility of flotation processing of copper-containing technogenic raw materials. Studies were performed using a sample of copper-containing tailings obtained after the processing of copper ore from the Kazakhstan deposit and a composite flotation reagent. The application of a combination of various collectors allows for raising technological indicators of flotation. The purpose of the research is the increase extraction of copper at flotation of copper-containing technogenic raw materials with the application of a composite reagent. The studied sample of tailings contains 0.23% of copper. A mixture of sodium butyl xanthate and thionocarbamate in the ratio of 1:1 was used as a composite flotation reagent. Parameters of flotation of copper-containing tailings were worked out: degree of regrinding, charges of sodium butyl xanthate, blowing agent T-92, and a composite reagent. Composite flotation reagent was fed into the flotation process of copper-containing tailings in the form of emulsion produced in a T 18 digital ULTRA-TURRAX dispersant. The optimal emulsification time of the composite flotation reagent was 1 min. Without emulsification in the composite reagent the percentage of particles smaller than 1.192 µm is 55.047%. After emulsification of the composite reagent for 1 minute, the percentage of particles smaller than 1.192 μm is 91.134%. In optimum basic mode the rough copper concentrate with a copper content of 4.2% was obtained with the extraction of 61.56%. With the use of a composite reagent, a blister copper concentrate with a copper content of 4.5% and a recovery of 66.54% was obtained. Extraction of copper increases by 4,98%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".