Laboratory Study of Flocculation and Pressure Filtration Dewatering of Waste Slurry
Bibliographic record
Abstract
In the process of large‐scale urban construction, large amounts of waste slurry are produced. The slurry has a high water content and is difficult to precipitate naturally, resulting in low treatment efficiency. To improve the treatment efficiency of slurry, a variety of inorganic and organic polymer flocculants were used to carry out flocculation settlement tests on the slurry. The changes in the slurry properties and the filtration dewatering effect after flocculation were tested. The results show that the addition of flocculant makes the slurry particles form aggregates, which leads to rapid precipitation of the slurry. The use of an inorganic flocculant significantly reduced the zeta potential of the slurry. Organic polymer flocculant, however, had little effect on the slurry potential, but did cause the slurry to produce larger size aggregates, resulting in a better flocculation effect than inorganic flocculant. Inorganic flocculants and organic flocculants can improve the pressure filtration dewatering performance of slurry. CPAM12 (cationic polyacrylamide, with a relative molecular weight of 12 million Daltons) had the best overall effect. The formation of aggregates after flocculation and the change in the nonuniformity coefficient ( C u ) were the main cause of improvement of pressure filtration dewatering performance of the slurry. When C u decreases from 11.85 to 8.75, the time required for pressure filtration stabilization is shortened by 70%. The nonuniformity coefficient of flocculated slurry can be used to evaluate flocculants, determine the optimal dosage, and predict the dewatering effect.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".