Evaluating the Behavior of Bauxite Tailings Dewatering in Decanter Centrifuges
Bibliographic record
Abstract
Depending on the ore quality, a washing process can be conducted with the bauxite, which basically consists of scrubbing the ore and screening in order to increase the available alumina grade, i.e., the alumina extractable using the Bayer Process, and reduce the impurity content. Tailings are usually disposed of in a tailings dam in the form of a slurry, which is a mixture of solid particles and liquid, consisting mainly of ultra-fine kaolinite, making the dewatering operation challenging. To reduce the environmental impact, mining companies are studying alternative methods to dewater the tailings, and different dewatering methods are available worldwide. The use of new technologies to dewater the tailings has contributed to facing the challenges of achieving sustainable development with their disposal. The decanter centrifuges are already an option for operations for the Canadian oil sands, gold ore in Peru, and nickel in New Caledonia; they are also being tested for iron ore in Brazil. In the present work, bauxite dewatering using the decanter centrifuge was evaluated to understand more about the behavior of these materials and to investigate the effects of various process parameters on the solid recovery and solid content of the flows, using three different kinds of equipment. The results indicated that decanter centrifuges can be used to achieve a high concentration of solids in the cake, with values ranging from 60% to 80% solids per weight and a great clarification in the liquid phase (centrate) from 0 to 6% solids per weight, values which mean the solid phase is suitable for reutilization in the processing circuit. Additionally, the present work provides a better understanding of how different solid contents feed can affect the behavior of the equipment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".