Testing and evaluation of modifying reagents in potash flotation
Bibliographic record
Abstract
Potash is the main source of Potassium which is one of the three basic plant nutrients along with nitrogen and phosphorus. Between 60 to 80% of the potash ore is processed by flotation, the other two important unit operations are hot or cold crystallization and electrostatic separation. Almost all the published literature on potash flotation refers to the insoluble minerals as "collector robbers" and increased collector usage is mentioned as the consequence of the presence of slimes in potash flotation circuits. The real effect of the presence of slimes in potash flotation is far more complex. The test program developed in the following chapters aimed to determine the most suitable modifying reagent(s) and the optimum dosage to optimize grade and recovery of KCI, and to evaluate various flocculants used in flotation desliming in order to determine the factors that affect flotation desliming efficiency. Based on the results, it can be stated that guar gum is probably the most effective insoluble slimes depressant for the ores under the conditions tested. The depressing action of the guar gum is strong even at low dosages (50 g/t). Carboxymethyl cellulose shows poor depressing ability at low dosages, with increasing depression performance at high dosages (200 g/t). Synthetic polyacrylamides can hardly be considered insoluble slimes "depressants" as a considerable amount of the insoluble slimes present in the feed report to the concentrate. Anionic flocculants increase insoluble slimes recovery when used instead of non-ionic flocculant at the same dosage. The recovery of KCI to the insolubles concentrate increases with the use of anionic flocculants as well but in a much smaller proportion. The flocculation-flotation of insoluble minerals is a flocculant dependent process, and the addition of a "insolubles collector" or a frother, or nothing at all merely affects the kinetics of the process.
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 | 0.000 | 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".