Wet screen performance prediction using coupled DEM and SPH: separation, wear and comparison to plant measurement
Bibliographic record
Abstract
• Large industrial scale wet screen performance is predicted using coupled DEM-SPH. • The screen cloth has around 30,000 apertures which are well resolved in the model. • Predicted slurry draining distance matches plant observations. • Discharge flow rate and oversize distribution agree with plant measurements. • Bed depth, resident screen load and wear patterns are sensitive to feed rate. A fully coupled multiphase DEM-SPH model is used to predict rock and slurry flow to better understand the performance of a very large industrial scale wet fine screen. Dependence on feed rate and the evenness of slurry flow onto the screen are investigated. The single deck banana screen is 8.22 m long and uses a cloth with combinations of rectangular slots and more complex shaped apertures. The model predictions are validated for the lower flow rate by comparing the draining distance for the slurry, the discharge mass flow rate and the screen oversize distribution with plant measurements yielding good agreement. The oversize mass fraction is found to be consistent with long-term site measurements while the predicted overflow size distribution has a very similar form to the measured data. These indicate that the modelling approximations and numerical resolution used in the model construction are suitable for obtaining good solution accuracy. Bed depth, resident load on the screen and wear patterns are found to be sensitive to feed rate but the flow field and general nature of the screen separation behaviour are not greatly influenced. Wear due to abrasion appears to be dominant in the upper half of the screen whereas impact damage is more uniformly distributed over the entire screen. At high feed rates, impact damage in the upper half of the screen becomes more significant.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".