Using Nickel as a Catalyst in Ammonium Thiosulfate Leaching for Gold Extraction
Bibliographic record
Abstract
The use of copper as a catalyst for gold leaching in ammonium thiosulfate solution might cause the high consumption of thiosulfate. Also, the high copper consumption is resulted in the zinc precipitation process for recovering the gold from the pregnant solution. In this investigation, nickel was used as a catalyst to minimize the reagent consumption. On a 100 mass%-75 μm of silicate type gold ore containing 16 g/t Au and 0.2 mass% of Fe and C, the nickel catalyzed ammonium thiosulfate solution could extract 95% of gold with the 1.2 kg/t-ore of ammonium thiosulfate consumption in 24 hours at the most favorable reagent combination of 0.0001 mol/dm3 NiSO4, 0.05 mol/dm3 (NH4)2S2O3 and 0.5 mol/dm3 NH4OH at pH9.5, while the standard cyanidation at 0.02 mol/dm3 (1.0 g/dm3) NaCN consumed around 1.5 kg/t-ore NaCN. In the concentration range of 0.0001∼0.005 mol/dm3 Ni2+, the ammonium thiosulfate consumption was 1∼5 kg/t-ore, while the ammonium thiosulfate consumption of copper catalyzed lixiviant was greatly increased from 3 kg/t-ore to 21 kg/t-ore as the increase of Cu2+ concentration from 0.0001 mol/dm3 to 0.001 mol/dm3. The feasibility of recycling barren solution was confirmed with zinc precipitation at nearly 100% of gold recovery. Nickel consumption on the cementation process was less than 50%. For extracting gold from the copper bearing sulfide ore, a higher ammonia and thiosulfate concentrations were required with 0.0001 mol/dm3 of Ni2+. The ammonium thiuosulfate consumption with nickel as catalyst on the copper bearing sulfide ore was about 1∼5 kg/t-ore less than that using copper as catalyst.
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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 | 0.001 |
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".