Numerical simulations of solid suspensions in a gravity thickener
Bibliographic record
Abstract
Reducing the overall water and energy consumptions in deep mines is a necessity in moving toward a more sustainable mining industry. Underground water treatment is a key requirement to achieve this goal. This study numerically investigates the dynamics of a continuous decantation process as a technique to recycle and reuse wastewater in deep mines without pumping it to the surface. A three-dimensional mathematical model that considers the conservation of mass and momentum has been derived, validated, and implemented to simulate the turbulent two-phase flow inside a decantation tank. The validation is achieved by comparing the numerical simulations to experimental data from the literature for two reference cases: (i) turbulent slurry flows (water and glass particles) in a horizontal pipe; (ii) turbulent swirling flow of limestone ore - water in a hydrocyclone. The framework of the validated model has been extended to examine the effect of various design parameters on the efficiency of a full-scale conical-shaped decantation tank (diameter of 1 [m] and total height of 0.97 [m]). The inlet values of flowrate and particle volume concentration are fixed to 50 [GPM] and 4.5 [%], respectively. The diameter and density of the solid particles are equal to 150 [μm] and 1.15, respectively. The results compare the efficiency of 20 different designs of the decantation tank (with/without inner cylinder, scrapers, feedwell, …) in terms of the overflow water quality. The results indicate that the efficiency of the decantation tank increases with inner surface area at fixed volume. Also the plain decanter with scrapers exhibits the highest efficiency, whereas the design with the center extract performs the worst.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".