Detailed assessment of inorganic fouling content and heterogeneity of activated carbon in CIP/CIL circuits to optimize regeneration strategies and improve gold recovery
Bibliographic record
Abstract
Gold recovery from low-grade ores relies predominantly on cyanidation followed by adsorption onto granular activated carbon (GAC) (Fleming et al., 2011)).However, inorganic fouling of GAC is believed to significantly reduce its gold adsorption capabilities and, consequently, process efficiency (Macrae et al., 1988;Smith et al., 1984).This study presents a methodology to characterize inorganic fouling on GAC used in carbon-in-pulp/carbon-in-leach circuits.A systematic protocol was developed to identify, quantify and map inorganic contaminant phases on GAC at the microscale using scanning electron microscopy-based automated mineralogy (Figure 1).This approach enables detailed assessment of inorganic fouling content and heterogeneity, while also providing crucial insights into its development and state.Furthermore, it allows for site-specific optimization of GAC regeneration strategies, promoting more sustainable resource use and ultimately contributing to improved gold recovery.
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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".