A compressional rheological approach to characterize biosludge with implications to dewatering
Bibliographic record
Abstract
Biosludge is a complex material, and it often presents challenges in industries such as the pulp and paper sector, particularly during dewatering processes. This study investigates the mechanical behavior of the biosludge using compressional rheology to understand the factors governing cake formation, permeability, and compressibility. Our findings reveal that increasing the solids concentration from ∼5 to ∼10 wt. % results in a substantial reduction in permeability, decreasing it by over an order of magnitude and thereby limiting dewatering efficiency. This increase in solids concentration also leads to a marked rise in compressive yield stress. Additionally, as compression progresses, the shear yield stress intensifies, and the storage and loss moduli become more pronounced. Consequently, as water is progressively removed, the material exhibits greater resistance to flow under applied stress. However, the addition of a positively charged polymer (cationic polyacrylamide) enhances dewaterability by increasing permeability, reducing compressive yield stress, and increasing the shear yield stress. Furthermore, the study identifies an optimal compression velocity crucial for effective water removal, balancing interconnected network formation and water migration. This study provides a fundamental understanding of the limitations inherent to the dewatering process and offers a quantitative framework to evaluate the role of polymer addition in enhancing dewaterability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".