Enzymatic Biooxidation Technology (EnBiTe): Process Optimization for Recovery of Gold from Refractory Sulfide Ores in Cold Conditions
Bibliographic record
Abstract
This study uses modular enzymatic systems to investigate the application of the Enzymatic Biooxidation Technology (EnBiTe) for gold recovery from refractory sulfide ores. The biooxidation process was optimized under various conditions, including substrate flow rates of 5–10 mL/h, circulation flow rates of 50–200 mL/h, and aeration flow rates of 1.0–2.0 L/min. The EnBiTe system utilized two immobilized enzyme modules: a glucose oxidase (GO) module to produce oxidizing agents and a catalase (CAT) module to mitigate oxidative stress by consuming excess hydrogen peroxide. These modules were organized in parallel and series configurations, and within the parallel configuration, they have demonstrated superior performance. Pyrite dissolution reached a maximum of 91% under optimized conditions with a substrate flow rate of 10 mL/h and a circulation flow rate of 100 mL/h. The recovery rate of gold (Au) using cyanidation was higher (90%) in the parallel configuration with a circulation flow rate of 100 mL/h. This research highlights the efficacy of modular EnBiTe systems as controllable and efficient solutions for enhancing gold recovery, offering significant advantages over conventional microorganism-assisted biooxidation methods.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".