Comparison of two online flocculation monitoring techniques for predicting turbidity removal by granular media filtration
Bibliographic record
Abstract
Particulate matter removal in drinking water treatment via direct granular filtration requires specific flocculation conditions (a process typically termed 'high energy flocculation'). Predicting filtered water turbidity based on flocculated water characteristics remains difficult. This study has sought to establish a relationship between filtered water turbidity and the flocculated water characteristics. Flocculation oflow-turbidity raw water was evaluated online using a Photometric Dispersion Analyser (PDA) and a Dynamic Particle Analyser in a modified jar test followed by a bench-scale anthracite filter. Coagulants used were alum, PASS100 and ferric sulphate, in addition to a polydiallyldimethylammonium chloride (polyDADMAC) cationic polymer. They were dosed in warm and cold waters, and flocculated with intensities (G) from 0 to 100 s(-1). Of the two instruments selected to analyse flocculation performance, the Dynamic Particle Analyser was shown to be the most sensitive, detecting small changes in floc growth kinetics and even floc growth under low flocculation conditions which remained undetected by the PDA. Floc size was shown to be insufficient in predicting particulate matter removal by direct granular filtration as measured by turbidity, although a threshold d(v) value (50 microm) could be identified for the test conditions evaluated in this project, above which turbidity was systematically lower than 0.2 NTU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".