The Separation of Carbonaceous Matter from Refractory Gold Ore Using Multi-Stage Flotation: A Case Study
Bibliographic record
Abstract
As a pre-treatment method of refractory gold ore, carbonaceous matter (C-matter) flotation was investigated with multi-stage flotation by rougher, scavenger, and cleaner stages. Different dosages of kerosene and MIBC (4-Methyl-2-pentanol) were applied and the optimum dosage was selected by testing in each flotation stage. With the combination of each stage, four circuit designs were suggested, which were a single-stage rougher flotation (R), rougher-scavenger flotation (R+S), rougher-scavenger-scavenger cleaner flotation (R+S+SC), and rougher-rougher cleaner-scavenger-scavenger cleaner flotations (R+S+RC+SC). The results indicated that the scavenger flotation increased C-matter recovery but reduced C-matter grade compared with single-stage rougher flotation. Cleaning of the scavenger flotation concentrate improved C-matter grade significantly, but reduced recovery slightly. Cleaning of the rougher flotation concentrate achieved overall improved selectivity in flotation. A combination of rougher-scavenger flotation followed by cleaning of both concentrates (R+S+RC+SC) resulted in 73% C-matter recovery and a combined cleaner concentrate grade of 4%; the final tailings C-matter grade was 0.9%, where the C-matter remaining in the tailings was locked, and fine grained. The results demonstrate the need for the multi-stage flotation of C-matter from refractory gold ore to achieve selective separation and suggested the potential of C-matter flotation as the pre-treatment for efficient gold production.
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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 | 0.000 | 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) | 0.002 | 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 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".