A cleaner production strategy for acid mine drainage prevention of waste rock: A porphyry copper case
Bibliographic record
Abstract
An in-process technology approach is proposed to identify the source of acid mine drainage (AMD) generation and prevent its formation in a porphyry copper waste rock (WR). Adopting actions before stockpiling the WR enables the establishment of potential contaminants and predicts the more convenient method for AMD prevention. A WR sample was separated into size fractions, and the WR’s net acid-generating potential was quantified using chemical and mineralogical characterization. The diameter of physical locking of sulfides (DPLS) was determined, and the fractions below the DPLS were desulfurized using flotation. Finally, the WR fractions and tailing from the flotation test were submitted to acid-base accounting and weathering tests to evaluate their acid-generating potential. Results show that the WR’s main sulfide mineral is pyrite, and the DPLS was defined as 850 µm. A sulfide recovery of 91% was achieved using a combination of HydroFloat® and Denver cells for a size fraction lower than DPLS. No grinding was conducted. The results show that size fractions greater than DPLS and the desulfurized WR are unlikely to produce AMD. The outcomes show that in-processing technology can be a more proactive approach and an effective tool for avoiding AMD in a porphyry copper WR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".