Rheological model for sludge dewatering with vacuum-assisted horizontal drains
Bibliographic record
Abstract
Prefabricated horizontal drains (PHDs) are increasingly popular for vacuum dewatering of sludge with high water content. However, traditional consolidation models based on the principle of effective stress and Darcy’s law fail to capture solid–fluid interactions in the early dewatering stages given the absence of a soil skeleton. To address this limitation, this study proposes a two-dimensional compression rheological model that simulates the coupled filtration–consolidation behavior of sludge dewatering, incorporating drag forces on solid/fluid, critical particle contact condition, and solid/fluid movement obstructions. The governing equation, derived from force balance, continuity, and kinetics equations, is solved using the alternative direction implicit difference method and verified through two one-dimensional cases, a laboratory model test, and a large-scale field trial. With unique constitutive relationships—compressive yield stress and hindered setting factor—the proposed rheological model presents more rational solid–solid and solid–fluid interactions compared to conventional consolidation models, particularly during filtration. Parametric analyses suggest that reducing horizontal or vertical spacing of the PHDs accelerates dewatering without affecting final soil deformation, while constitutive parameters influence both the dewatering rate and final vertical strain of the soil in distinct manners.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".