Vanadium recovery with barium slag as a roasting additive: A novel waste-to-value approach
Bibliographic record
Abstract
Vanadium slag (V-slag), a byproduct of iron smelting, is the primary industrial source of vanadium. In this study, we propose a novel ‘Waste-to-Value’ approach by utilizing barium slag (Ba-slag)—a residue generated in the production of BaCO 3 from barite—as an additive in the roasting process of vanadium extraction. V-slag was mixed with Ba-slag, roasted in air, and subsequently leached with sulfuric acid. A series of leaching experiments were conducted, varying additive ratios, roasting temperatures, and holding times to identify optimal conditions. The ideal parameters—an additive ratio of 0.5, roasting temperature of 900 °C, and a holding time of 2 hours— achieved a leaching efficiency of 93.65 %, which is comparable to the reported sodium and calcification methods using pure chemical additives and demonstrates the effectives of the present method. Comprehensive characterization and analysis results confirmed that the mechanism behind using Ba-slag as a vanadium roasting additive involves the oxidation of V 3+ within the spinel phase to V 2 O 5 , which then reacts with BaO—primarily produced from BaCO 3 decomposition—to form leachable Ba 3 (VO 4 ) 2 . Additionally, we separated the overall reaction process by deconvolution of the DTG curves, from room temperature to 1000 °C, into several stages, including fayalite oxidation, spinel oxidation, and barium slag devolatilization. Finally, we determined the activation energy and kinetic model function of each process based on the Kissinger-Akahira-Sunose (KAS) method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".