Copper recovery from nickel laterite with high‐iron content: A continuous process from mining waste
Bibliographic record
Abstract
Abstract The waste product from the hydrometallurgical processing of nickel laterite ores can contain valuable metals, making their recovery economically viable. However, the high‐impurities content, mainly iron, makes the process technically unfeasible. As a result, the separation of metals from the leach solution must be selective. Among the techniques available, the use of chelating resin is advantageous due to its selectivity and low energy consumption. Among the commercial chelating resins available, Dowex XUS 43605 has been shown to be highly selective for copper and can be used with a high impurities content. Although there are studies on the use of Dowex XUS 43605, none have evaluated a high impurities content and modelled a continuous process. For this reason, the aim of this work was to investigate copper recovery by a continuous process. The Dowex XUS 43605 chelating resin with HPPA functional group was used in ion‐exchange experiments. Column experiments were performed in two steps: loading (to recover copper) and elution (to obtain a copper‐rich solution). The removal of iron and the subsequent collection of copper were possible in a precipitation step using CaCO3. The results showed that the solution obtained from elution had a copper concentration that was 10 times higher than in the loading. All of the iron was removed from the elution solution at pH 3.5 with 5% of copper losses. Copper precipitation was possible at pH 5.5. From the results obtained, a proposed flowsheet for recovering copper was suggested.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".