Investigation of the Flotation of an Ore Containing Bastnaesite and Monazite: Kinetic Study and Process Flowsheet Simulation
Bibliographic record
Abstract
Laboratory flotation tests carried out using an ore sample containing Rare Earth Elements (REEs) present as monazite and bastnaesite show that the flotation of monazite is slower and yielded lower recovery than that of bastnaesite. Results show that when studying the performances of a concentration process for an REE ore, it is essential to not look only at the behavior of the individual REEs but to convert elemental assays into mineral assays to obtain the mineral’s actual response to the concentration process. The results of the laboratory flotation tests are used to calibrate a flotation simulator applied to study different circuit configurations for the concentration of the REE minerals. Indeed, it is shown that for the studied ore, two cleaning stages of a rougher concentrate are sufficient to produce a concentrate with a Total Rare Earth Oxide (TREO) grade above 40%, which is acceptable for the subsequent hydrometallurgical process. The simulation also shows that it may be feasible, if required for the hydrometallurgy step, to separate bastnaesite and monazite by taking advantage of the different flotation kinetics of the two minerals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".