Bibliographic record
Abstract
Shear intensity, shear time, and polymer dose are the main parameters that determine the dewaterability of wastewater sludge. Polymer dose required to condition the sludge increases with the increase of shear intensity (G) and shear time (t). Therefore, in order to minimize the polymer demand during conditioning and dewatering, shear should be optimized. Optimization of shear can be achieved if the total shear that the sludge network is exposed to during conditioning and dewatering can be measured and quantified. This is quite a challenge since total shear includes unintended shear introduced during piping and pumping, and currently there is no direct or indirect technique that can measure this unintended shear. Unintended shear increases the polymer demand and shifts the optimum polymer dose to a higher dose, which in turn decreases the cake solids concentration and the efficiency of the dewatering process. Thus, quantification of the unintended shear and adjustment of the polymer dose accordingly are essential for the optimization of dewatering processes. The main objective of this study was to develop a method for sludge shear measurement based on the rheological characteristics of sludge and illustrate its possible applications at treatment plants. The results of this study indicate that the rheological characteristics of sludge can be used to estimate an unknown amount of shear that sludge network is exposed to, and to match the jar-test mixing conditions to that of the full-scale mixers employed at treatment plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".