A mineralogical investigation into the beneficiation of a rare-earth mineral deposit using physical separations
Bibliographic record
Abstract
This thesis examines the application of physical separations to the Nechalacho deposit in the Northwest Territories of Canada. It is specifically focused on relating the deposits mineralogical characteristics to quantify mineral separation behaviour in various processes; and ultimately proposing an industrially applicable beneficiation process for the ore. The valuable REE-bearing minerals in this ore are allanite, bastnӓsite, columbite (Fe), fergusonite, monazite, synchysite and zircon; and the primary gangue minerals are quartz, feldspars and iron oxides.Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) results indicate the grain size distributions and association behaviour of valuable minerals in the deposit cause them to be concentrated in high specific gravity (SG) particles, at particle sizes well above their liberation size. To take advantage of this property, a Knelson Concentrator and spiral are examined as methods to preconcentrate the ore at a relatively coarse particle size; with the goal of rejecting low SG silicate gangue minerals (quartz and feldspars) early in the beneficiation process. Both techniques are determined to be effective. However, due to its simplicity, the spiral is recommended as the more applicable process in an industrial setting. The optimization and application of such a process could have profound effects on any downstream processing, as well as in the overall economics, as it would minimize the energy required for comminution. Following the preconcentration test work, a Knelson Concentrator, Multi-Gravity Separator (MGS) and Mozley Laboratory Shaking Table are studied to assess their ability to produce a bulk heavy mineral (REM, zircon and iron oxides) concentrate, at particle sizes much closer to the liberation size of the valuable minerals. All three techniques are effective at upgrading zircon and REM. But, the MGS is recommended as the superior process. The MGS is particularly effective at recovering and concentrating zircon. Although REM are effectively upgraded, their recovery was relatively low. Mineralogical analysis indicates that with optimization of the comminution process and the MGS operating conditions, satisfactory recovery targets are likely to be achieved. However, it is noted that allanite recovery may remain depressed due to its relatively low SG compared to the other valuable minerals in the Nechalacho deposit. Gravity concentrates from the MGS and Mozley Laboratory Shaking Table are then processed using a low-intensity magnetic separator, to remove iron oxide gangue, and a wet high intensity magnetic separator (WHIMS), to produce separate REM and zircon concentrates. Iron oxide gangue is effectively removed. But, its association with REM and zircon indicates that some losses of valuable material in this step may be unavoidable. The WHIMS is capable of producing a high-grade REM concentrate; with the relative magnetic response of REM following allanite > fergusonite > columbite (Fe) > monazite > bastnӓsite > synchysite. However, a significant portion of REM remain in the non-magnetic fraction with zircon. Concluding that a magnetic separation step which can induce a high magnetic force on REM through high magnetic field gradients should be examined. These metallurgical tests coupled with the use of automated mineralogy, along with other characterization techniques, led to the eventual proposal of a flowsheet to beneficiate the Nechalacho deposit
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".