Diethylenetriamine as a selective pyrrhotite depressant: Properties, application, and mitigation strategies
Bibliographic record
Abstract
Abstract Pyrrhotite (Po) is an abundant iron sulphide mineral that occurs in nickel sulphide deposits, such as those in Sudbury, Ontario. Due to its low economic value and its high contribution to SO2emissions produced during smelting, Po is rejected into the tailings during mineral processing. The rejection of Po can be accomplished in several ways, including by using chelating agents such as diethylenetriamine (DETA) as depressants in froth flotation. DETA can significantly improve nickel recovery and concentrate grades; however, it is challenging to manage in the tailings area. DETA, when used, forms stable chelates with Ni2+and Cu2+which cannot be precipitated with traditional lime treatment. Once in the tailings management area, DETA can desorb from Po solids upon dilution or with changes in temperature and/or pH. The species chelated with DETA can also change depending on the availability of the ions in solution. The use of DETA can, therefore, lead to concentrations of Ni2+and Cu2+in final effluent that exceed the regulated amounts. Due to the challenges with chelated metals in effluent, it is recommended that the chelating agents be removed from the tailings stream before it is deposited. Although DETA‐metal chelates are rarely discussed in the literature, some mitigation and degradation strategies investigated by industry are discussed in this review. DETA degradation methods are also discussed, with the primary focus being on biodegradation. DETA has generally been described as recalcitrant to biodegradation although there is evidence that proper acclimation should allow bacteria to develop DETA degradation pathways.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".