Recovery of Cu(II) from nickel laterite leach using prereduction and chelating resin extraction: Batch and continuous experiments
Bibliographic record
Abstract
Abstract The processing of laterite ores for nickel and cobalt production is increasing to meet the global demand for these metals. Sulphuric acid is used as a leaching agent, and metals present in the solution may be recovered by many different processes, such as ion exchange. The main problem with nickel limonite layer leach solution is the high concentration of iron, which decreases the efficiency of resin adsorption of nickel and cobalt. The removal of iron by oxidation and precipitation results in nickel, copper, and cobalt losses (co‐precipitation). The aim of this work was to investigate the chelating resin extraction to recover copper from a leachate combined with a pre‐reduction process, in order to increase the resin's efficiency and to increase its pH above 2.00. The following three synthetic solutions were studied: first, a solution prepared with Fe(III); the second solution was prepared with Fe(II); and the last solution was prepared with Fe(III) using a reducing process. Batch experiments were performed to study the influence of pH and temperature, and column experiments with three solutions were compared in order to verify suitable conditions to recover Cu(II) in a fixed‐bed column process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".