Recovery of nickel and cobalt from nickel laterite leach solution using chelating resins and pre‐reducing process
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The aim of this work was to recover nickel and cobalt from a nickel laterite leach solution using chelating resins combined with a pre‐reducing process. Sodium sulphite was used as a reducing agent to convert Fe(II) from Fe(III) and increase adsorption efficiency. Batch experiments were performed using synthetic solutions to study the effect of pH in recovering these metals using chelating resins Lewatit TP 207 and Lewatit TP 220. Column experiments were performed to simulate the fixed‐bed column process in the following two steps: first, removing copper; and second, recovering nickel and cobalt. Two acids were tested as eluent, namely, sulphuric and hydrochloric acid 1M and 2M. Batch experiments showed that increasing the recovery of the metals accompanied an increase in pH. Copper recovery was maximal at pH 2.00, and the resin selectivity changed in pH above 2, decreasing copper adsorption. However, batch experiments showed that nickel and cobalt recovery was higher at pH 3.50, and resin adsorbed a high concentration of contaminants such as iron, zinc, and chromium. For this reason, nickel and cobalt recovery at pH 2.00 was better in column experiments, with less of the contamination in the metals being adsorbed by the resin and a high selectivity for nickel and cobalt. Hydrochloric acid 2M showed to be more efficient as eluent than sulphuric acid. A precipitation process using NaOH was used to remove contaminants present in the eluent solution, and Cyanex 272 was used to separate cobalt and nickel through the solvent extraction process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it