Impact of polymer-sludge interaction on rheogram peaks and optimum dose determination
Bibliographic record
Abstract
Torque rheometry offers potential for in-line monitoring use, screening polymers, and on site optimization of polymer dose by treatment plant operators. This study investigates the peaks formed following direct polymer injection into sludge during rheological analysis. The peaks enable observation of both flocculation and deflocculation phases, and the highest point of the peaks indicates the point where network bonds rupture. The goal of this study was to test the method reported by Ormeci on anaerobically digested sludge samples with several polymers, covering a wide spectrum of molecular weights and cationic charges, to establish a better understanding of how polymer-sludge interaction affects torque-time rheograms and peaks observed after polymer injection. The results from this study indicate that peaks can potentially be used to select the best performing polymer and identify the optimum polymer dose. In the underdose region, the peaks increase in size with increasing polymer dose until the optimum dose is reached. Once the optimum dose is reached, the peaks may decrease in size and then increase again, or stay same size until the polymer dose is very high. In the overdose region, very large and erratic peaks are observed which can be explained by the presence of unmixed polymer in the sample.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".