Development of a Large Batch Bench-Scale Dissolved Air Flotation System for Drinking Water Treatability Tests
Bibliographic record
Abstract
The dissolved air flotation (DAF) has been used in drinking water treatment for its excellent algae and natural organic matter (NOM) removal. DAF drinking water treatability test are often conducted in a DAF jar test apparatus. Although, DAF jar test studies showed that they were able to predict NOM removals at full-scale facilities well, they have not always been successful in predicting the turbidity removals. One possible reason of the DAF jar test inaccuracy results could be associated to the small jar diameter, which may create wall effects. Therefore, the first two objectives of this research are: a) to develop and test a new, larger diameter and larger volume batch bench-scale dissolved air flotation system (LB-DAF) to better simulate turbidity removals in drinking water applications; b) to confirm these results by comparing the LB-DAF and full-scale DAF turbidity removals for two other source waters. The raw water characteristics of the three plants were quite different and the testing was performed at different times of the year. The development/optimization of the LB-DAF evaluated the impact of different variables (i.e., mixing intensity, water depth/tank diameter ratio, impeller shape, saturator pressure and recycle ratio). The results showed that the LB-DAF predicted well the full-scale DAF turbidity removals at three water treatment plants, and these predictions were better than those of DAF jar tests. For the LB-DAF design and operational variables evaluated had a limited impact on the turbidity removals. The LB-DAF predicted well DAF full-scale turbidity removals regardless of water temperature. This is an indication of the robustness of the DAF system. Ballasted sedimentation (BS) is a compact coagulation/flocculation and sedimentation process combination that has become very popular because it is very compact and because it can handle large variations in raw water turbidity and NOM. The literature survey did not initially identify studies on the BS treatment of algal impacted waters, for which DAF is considered particularly suitable. Thus, the third main objective of this dissertation was to compare the efficiency of BS with that conventional gravity settling (CGS), and that of DAF for the treatment of an algal impacted water via jar tests. These comparisons were performed at the Belleville Water Treatment Plant using Bay of Quinte water, one of the most eutrophic zones of Lake Ontario. Unfortunately, a change of weather prior to the testing resulted in raw water samples with relatively low concentrations of algae and cyanobacteria. The testing showed that DAF and BS had very similar NOM, cyanobacteria/algae (chlorophyll a and phycocyanin) removals.; however, the BS required microsand addition, polymer addition and a slightly higher alum dose. Only for turbidity removal the DAF was somewhat superior. It is suggested that these comparison experiments be repeated with waters that are more impacted by algae and cyanobacteria.
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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 | 0.001 | 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) | 0.001 | 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 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".