MANAGING THIOSALTS IN MILL EFFLUENTS 1 "STUDIES CONDUCTED AT THE KIDD METALLURGICAL SITE"
Bibliographic record
Abstract
Grinding and flotation of sulphide (S2 2- ) ores containing pyrite (FeS2) and pyrrhotite (FeS) in alkaline conditions produces a series of partially oxidized sulphur oxyanions (SxOy 2- ) with the most common being thiosulphate (S2O3 2- ), trithionate (S3O6 2- ) and tetrathionate (S4O6 2- ), which are collectively called Thiosalts. Oxidation of continues in the solution pulp or effluent until the end product of sulphate (SO4 2- ) is reached. Since oxidation reactions also produce proton (H + ), represent delayed acidity in effluents with the potential to cause a drop in pH within the treatment system and in the downstream environment. Thiosalts generation is site-specific and current processing technology has not been able to cost effectively prevent their production. As natural degradation in tailings ponds may not be sufficient to fully manage oxidation, some sites may require implementation of additional measures due to the presence of site constraints or conditions such as short retention time and cold climate. Currently known best thiosalts practices include: increasing retention time in tailings ponds; improving thiosalt oxidation rates through optimization of pond pH's prior to discharge; increasing alkalinity and/or buffering capacity in effluents; and treating water using chemical or biological oxidation or biological (sulphate) reduction processes. Some sites practice addition of excess buffering/alkalinity in the treated water as a cost-effective option to delay and compensate for the acid generation. However, results can be variable during different times of the year and development and use of alternative methods are required. Hydrogen peroxide as a chemical oxidation method is often chosen due to its low capital cost, high reactivity and non-toxic by-product generation. The Xstrata Copper Canada, Kidd Metallurgical Site in Timmins, Ontario has investigated possible options to manage by conducting laboratory tests and modeling studies. The studies conducted and the management methods implemented for the site are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".