Selective separation of copper and nickel ions from aqueous solutions containing calcium by emulsion liquid membranes using central composite design
Bibliographic record
Abstract
Abstract The emulsion liquid membrane technique was utilized to selectively extract copper and nickel from a synthetic aqueous solution containing calcium, which was used to mimic a tailings stream found in the Sudbury region of Canada. The results showed copper and nickel ions were successively extracted from the synthetic solution. Two central composite designs and an analysis of the experiments were used to optimize the process and determine the main effects and interactions of experimental factors. In the first stage, copper was extracted with a minimum removal of nickel and calcium. It was found that under optimum conditions 98 % of the copper was extracted, with only 0.9 % of the nickel and 1.3 % of the calcium being extracted. The subsequent copper stripping efficiency was 95.7 %. In the second stage, the remaining aqueous solution was treated to remove nickel with minimum calcium removal. During this stage, the corresponding nickel and calcium removal percentages were 99.0 and 0.55 %, respectively, with a nickel stripping efficiency of 84.1 %. Laboratory bench‐scale tests using a two‐stage mixer‐settler showed a good correlation with these results when moving to a semi‐continuous process, which extracted 99.7 % of the copper and 98.2 % of the nickel, with only 2.2 % calcium extraction.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".