A Comparison Between Conventional and Microwave Heating for the Thermo-concentration of Nickel from Pyrrhotite Tails
Bibliographic record
Abstract
In nickel sulphide ore deposits, nickel is found in one of two minerals: pentlandite, which contains approximately 30 wt% Ni, and pyrrhotite, where the nickel content is less than 1 wt% Ni. Rapid growth in the demand for nickel in the second half of the 20th Century, combined with more stringent environmental regulations, led to the removal of pyrrhotite from the smelter feeds and its disposal in tailings. Pyrrhotite remains an attractive resource as global nickel demand continues to increase. One proposed process for extracting the nickel is thermo-concentration. Thermo-concentration recovers the nickel to a ferronickel alloy with sulphur remaining in the sulphide. Significant heat input is required as the process is run at temperatures above 800°C and the reactions are endothermic. This thesis explores the use of microwave radiation to supply this heat and compares it to a conventional resistance furnace. Thermo-concentration was performed using pyrrhotite tails from Sudbury, Ontario; two iron-bearing materials from Custer, South Dakota and Clear Hills, Alberta; and metallurgical coke. The pyrrhotite tails were used in the as-received condition and in two upgraded forms. Upgrading of the tails was performed by magnetic separation and collectorless flotation followed by magnetic separation. Microwave tests were conducted using 800 W of input power and 120 and 300 seconds of processing time. Conventional furnace tests were performed at 900°C for 35 minutes. Limited formation of ferronickel alloy particles occurred; sample metallization was less than 10% for all samples and particles that did form were too small to be easily recovered by common mineral processing techniques. Three particle populations were found of low (less than 3 wt% Ni), medium (4-23 wt% Ni) and high (greater than 18 wt% Ni) grade, all of which appeared to have formed from the solidification of a liquid matte. It is recommended that thermo-concentration trials be conducted at longer reaction times to permit further metallization.
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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 | 0.000 | 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) | 0.001 | 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".