Enhancement of biosludge dewatering using proteins through dual conditioning
Bibliographic record
Abstract
Abstract In pulp and paper mills, effective biosludge dewatering is essential in wastewater treatment to reduce the large volume of biosludge that needs to be treated and disposed. The dewatering process normally requires the use of polymers from petroleum‐based sources. This study explores the potential of using cationic proteins such as protamine for biosludge dewatering through dual conditioning with a small amount of a synthetic anionic polymer such as anionic polyacrylamide (APAM). The results show that dual conditioning provides substantial synergistic enhancements in dewatering. The maximum cake solids content of biosludge achieved by adding protamine (7.5%) alone was 12%. By dual conditioning with a small amount of APAM (0.1%), not only the cake solids content was increased to 16%, but also the amount of protamine addition was substantially lowered to 2%. These results, coupled with the change in zeta potential of the particles in the biosludge samples, suggest that the cationic protamine reduced the negative charge of the particles, allowing smaller particles to agglomerate and providing a positively charged framework for the subsequent addition of the negatively charged APAM. After adding APAM, substantial floc‐bridging occurred, allowing smaller flocs to aggregate into larger flocs. These synergistic effects can lower the wastewater treatment cost by reducing the amount of synthetic polymer and by applying low‐value proteins from natural sources.
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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) | 0.001 | 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 | 0.001 |
| 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".