A Novel Electrochemical Approach to Study the Interaction of Blinding Agents with Preg-Robbers in Carbonaceous Gold Systems
Bibliographic record
Abstract
Preg-robbing usually refers to the adsorption of a gold cyanide complex, i.e. Au(CN)2 −, on the components of a gold ore including carbonaceous materials and other minerals during the gold leaching process (1-3). The adsorption of gold ions on carbonaceous materials is one of the most common phenomenon responsible for gold losses from the cyanide leaching process (1, 4). Depending on the carbon content of the ores, gold losses may reach 90 % of leachable gold due to preg-robbing during cyanidation (5). Chemical blinding agents have previously been investigated to passivate the surface of carbonaceous preg-robbers in the ores, thus reducing the extent of preg-robbing and allowing an increase in gold recovery (1, 3, 6, 7). However, the mechanism by which such agents suppress the ion-adsorption capability of the carbonaceous materials as well as their interactions with other components of the ore, including pyrite, and more importantly, gold, are yet to be fundamentally investigated. The latter process could severely affect final gold recovery if the surface of the gold becomes passivated. This study investigates the effect of various blinding agents on the main components of a gold-containing concentrate: carbon, gold and pyrite. A novel electrochemical approach is employed, i.e. double-layer capacitance measurements combined with electro-oxidation/reduction peaks obtained from cyclic voltammetry tests. Our group is the first to employ the electric double-layer theory to study the preg-robbing capacity of carbonaceous ores with total carbonaceous matter (TCM) contents as low as 0.3 % by weight. Figure 1 shows a comparison between the blinding activity, and durability, of various surfactants and chemical reagents, at different concentrations, on the surface of activated carbon particles over a 24-hour contact period. The capacitance is an indication of how well the chemical reagents have been adsorbed on the activated carbon particles with the lowest capacitance values showing the best surface adsorption. The study reveals that kerosene, Lecithin and Polymax 30 are among the best blinding agents for the activated carbon surfaces in terms of adsorption and durability. Further cyclic voltammetry studies on gold surfaces in alkaline cyanide solutions (with the added blinding agents) have shown that kerosene is the least detrimental to the gold cyanide leaching process, i.e. the electrochemical oxidation reaction for the formation of AuCN complex. Figure 1. A comparative study for the effect of various chemical reagents, i.e. Aristonate H and L, Lecithin, Sodium dodecyl sulfate (SDS), kerosene and Polymax 30, on the ion adsorption capability of the activated carbon particles over 24 hours. References: J. D. Miller, R. Y. Wan and X. Díaz, in, p. 937 (2005). K. L. Rees and J. S. J. van Deventer, Hydrometallurgy, 58, 61 (2000). R. Dunne, W. P. Staunton and K. Afewu, A historical review of the treatment of preg-robbing gold ores – what has worked and changed, in World Gold 2013, p. 99, The Australasian Institute of Mining and Metallurgy, Melbourne (2013). R. Dunne, K. Buda, M. Hill, W. Staunton, G. Wardell-Johnson and V. Tjandrawan, Mineral Processing and Extractive Metallurgy, 121, 217 (2012). J. F. Stenebraten, W. P. Johnson and J. McMullen, Characterization of Goldstrike ore carbonaceous material Part 2: Physical characteristics, in, p. 7, SOC MINING METALLURGY EXPLORATION INC, LITTLETON (2000). P. M. Afenya, Minerals Engineering, 4, 1043 (1991). M. D. Adams and A. M. Burger, Minerals Engineering, 11, 919 (1998). Figure 1
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".