A sustainable chromium and vanadium extraction and separation process from high-chromium vanadium slag via synchronous roasting and asynchronous leaching
Bibliographic record
Abstract
• Achieves comprehensive recovery of HCVS and separation of V and Cr. • SRAL approach simplifies V and Cr extraction into one process. • Mixed reagents combine sodium salt and calcium methods, avoiding their drawbacks. • Thermodynamic analysis confirms the feasibility and mechanism of the new method. • The method is energy-efficient, simple, quick, and sustainable for industrial use. Vanadium and chromium are critical industrial resources, with approximately 88% of global vanadium production originating from vanadium slag. Due to their similar chemical properties, chromium tends to concentrate in vanadium slag during the smelting process. This issue is especially pronounced in high-chromium vanadium slag (HCVS), where the chromium content is significantly elevated, presenting substantial challenges for the recovery and separation of vanadium and chromium. Traditional sodium salt and calcium-based roasting methods, while effective for conventional vanadium slag, are not directly applicable to HCVS due to the concerns on separation difficulties, energy consumption, and operational complexity. To address these challenges, this study introduces a synchronous roasting and asynchronous leaching (SRAL) process, incorporating a strategy for preferential chromium extraction, to achieve efficient recovery and separation of vanadium and chromium. The process employs a binary mixture of calcium oxide and sodium carbonate for roasting, which transforms vanadium into water-insoluble calcium vanadates and chromium into water-soluble sodium chromates simultaneously, followed by chromium extraction through water leaching and vanadium extraction via ammonium bicarbonate solution leaching. The experiment indicates that the maximum leaching efficiencies achieved in this study are as follows: the water leaching efficiency of vanadium is 8.43%, the ammonium bicarbonate leaching efficiency is 86.52%, resulting in a total leaching efficiency of 94.95%. For chromium, the water leaching efficiency is 84.23%, the ammonium bicarbonate leaching efficiency is 5.42%, leading to a total leaching efficiency of 89.65%. Thermodynamic analysis and experimental validation demonstrate the feasibility of the method from both theoretical and practical perspectives, offering a more efficient and sustainable approach for the recovery and utilization of HCVS and ensuring the sustainability of vanadium and chromium.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".