Advances in reagent systems and mechanisms for desilication from bauxite via flotation
Bibliographic record
Abstract
Abstract The imbalance between supply and demand for bauxite resources, coupled with inadequate refining process compatibility, poses significant challenges in fulfilling the raw material requirements for the Bayer process, thereby severely hindering the sustainable development of the aluminum industry in China. Consequently, low alumina-to-silica ratio bauxite necessitates pretreatment to enhance its quality, ensuring compliance with the feedstock specifications of the Bayer process. Flotation technology, emerging as an efficacious desilication pretreatment approach, has garnered considerable attention and demonstrated substantial application potential in bauxite desilication. This study comprehensively analyses the chemical composition and mineralogical characteristics of bauxite, systematically elucidating and contrasting the advantages of direct and reverse flotation collectors and auxiliary reagents. Furthermore, it delves into the distinct mechanisms of action these reagents exhibit with diaspore and aluminosilicate minerals. Building upon this foundation, the study offers insights and projections for future research endeavours in bauxite flotation desilication, which holds profound theoretical significance in addressing the trend of depleting bauxite resources in China.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".