A developing novel alternative bio-oxidation approach to treat low-grade refractory sulfide ores at circumneutral pH
Bibliographic record
Abstract
The extensive neutralization required in acidic bio-oxidation, a conventional pretreatment for low-grade refractory matrices in the gold industry, constitutes one of the principal drawbacks due to the large volume of waste streams. Performing an oxidative pretreatment at circumneutral pH with an in-situ neutralization would avoid the production of undesirable waste, causing potential economic and environmental advantages. For the first time, this investigation evaluates a novel process involving a biological oxidative pretreatment for low-grade refractory ore using two biosafety level 1 neutrophilic microorganisms encompassing Thiobacillus thioparus and Starkeya novella at near-neutral pH. Optimal bacterial growth conditions were determined regarding the culture medium and initial energy source using UV-visible and manual cell counting (cells/mL). Thereafter, biological oxidation of different matrices, including first elemental sulfur and subsequently a refractory sulfidic ore, was evaluated in batch flask cultures and then scaled up into a bioreactor using optimal experimental conditions. Results revealed that culture media containing ca. 4.5 and ca. 0.9 g/L thiosulfate favored biological oxidation of the refractory sulfidic ore using T. Thioparus and S. Novella, respectively, which led to corresponding sulfide oxidation of 27 and 14% within 10 days, comparable to reported studies. The biological action was confirmed by C/S detector and SEM technique of pre- and post-pretreatment residues. Overall, this research is a step forward to advance the understanding of a biological pretreatment out of the highly acidic pH range, promoting the view of a net-zero target by potentially reducing the production of more significant waste streams compared to conventional operations.
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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".