Selective removal of copper and nickel ions from synthetic process water using predispersed solvent extraction
Bibliographic record
Abstract
Abstract Selective predispersed solvent extraction and the stripping of copper and nickel ions from synthetic process water containing calcium ions was investigated, and the effect of four experimental parameters on extraction efficiency was studied. The extraction process was performed in two stages. In the first stage, maximum copper extraction was targeted with nickel extraction minimized. During the second stage, nickel was extracted from the remaining copper‐free aqueous solution and optimum experimental conditions for maximum nickel recovery were determined. In order to investigate the effect of experimental parameters (extractant concentration, phase volume ratio (PVR), equilibrium pH, and calcium concentration) on the nickel extraction, response surface methodology (RSM) was used. It was found that equilibrium pH, extractant concentration, and calcium concentration were the most significant factors on nickel extraction. While the first two factors had a positive effect, the latter one negatively affected the response. In order to selectively extract copper, the optimum range for the maximum extraction of copper with minimum nickel extraction was determined, which for any level of calcium concentration is an extractant concentration of 0.2–0.3 % (w/v), phase volume ratio of 2, and equilibrium pH of 2.5. Once copper ions were selectively removed from the process water, the optimum conditions for maximum nickel concentration were determined at three levels of calcium concentrations. Stripping experiments were also carried out, and the optimum acid concentrations of 10 and 5 g/L were determined for the stripping of copper and nickel, respectively.
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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 | 0.000 |
| 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 | 0.000 |
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".