A Mean Phi Model for Pressure Filtration of Fine and Colloidal Suspensions
Bibliographic record
Abstract
A working model for engineering analysis of pressure filtration is presented. Based on the filtration characteristics of fine and colloidal suspensions, the process was divided into two stages. A time-invariant spatially uniform volume fraction of solids approximation is invoked in the growing filter cake stage (stage 1). A time-dependent spatially uniform volume fraction of solids assumption is made in the cake consolidation stage (stage 2). The two models, named collectively as Mean Phi (M-P) model, have a common physical basis, seamless continuity between the stages and internal consistency. The M-P model has only three parameters: terminal or equilibrium volume fraction of solids in the filter cake that is related to its compressive yield stress, critical volume fraction of solids, which joins stage 1 and stage 2, and a permeability factor, which is common to stages 1 and 2. The model is validated with a large number of colloidal suspensions filtered under highly diverse physical-chemical process conditions. A Pareto profile is identified that relates the timescale of filtration and the extent of dewatering achieved, the two most important performance indices of the process.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".