Large batch bench-scale dissolved air flotation system for simulating full-scale turbidity removal
Bibliographic record
Abstract
One of the expected outcomes of global warming is increased algal and cyanobacterial blooms. Based on its ability to separate algal particles, dissolved air flotation (DAF) is considered as a climate change adaptation technology for water treatment. The feasibility of DAF treatment is often assessed using DAF jar tests; however, they are not particularly good at predicting a full-scale DAF system's turbidity removals. Therefore, our group has developed a more reliable larger-diameter/larger-volume batch apparatus (LB-DAF), which was optimized by comparison with a full-scale DAF plant treating a low turbidity, highly coloured river water (SUVA ∼ 4.3). The objective of this study was to verify that the LB-DAF was capable of simulating full-scale DAF systems treating two significantly different waters. One was water from a large eutrophic bay in Lake Ontario (SUVA ∼2.6) and the second was a river water (SUVA ∼3.5). The turbidity removals achieved by the full-scale DAF systems treating these waters were compared with those for the LB-DAF tests conducted using different flocculation velocity gradients, saturated water pressures, recycle ratios and water depth to diameter ratios. The LB-DAF tests are good predictors of the full-scale DAF turbidity removals, the average difference for the two waters tested were 2% and 6%. The LB-DAF natural organic matter (NOM) removals for both waters differed by less than 1% from that measured at the corresponding treatment plants. In addition, as in our previous LB-DAF study, varying the different LB-DAF operational variables did not have a significant impact on turbidity and NOM removals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".