Extraction of vanadium from direct acid leach solution of converter vanadium slag
Bibliographic record
Abstract
Direct acid leaching of converter vanadium slag by titanium dioxide waste is eco-friendly and efficient, but with low selectivity. This novel technique can result in a vanadium solution which contains chromium(III), aluminium(III), magnesium(II), manganese(II) and high amount of iron(II) and iron(III). Bis (2-ethylhexyl) phosphoric acid (D2EHPA) and tri-butyl-phosphate (TBP) diluted with sulphonated kerosene were applied for vanadium extraction from the multi-element leach solution. The effects of the initial pH, concentration of D2EHPA, ratio of organic to aqueous phase, and the extraction time on the extraction efficiency of vanadium were investigated in saponification and unsaponifiable systems, respectively. The results showed that the vanadium extraction percentage can be up to 97% and the iron extraction percentage can be less than 10% in a thirteen-stage counter-current simulation test and the separation coefficient between vanadium and iron can reach to 109.8. Furthermore, vanadium(IV) can also be separated from other impurities such as aluminium(III), magnesium(II), manganese(II), chromium(III) efficiently. The loaded organic phase was stripped by 184 g L−1 sulphuric acid solution in a three-stage counter-current stripping process and with the total vanadium stripping percentage of greater than 99.5%. In the end, the vanadium pentoxide products with a purity of 99.14% were obtained.
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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) | 0.000 | 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) | 0.001 | 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".