Investigation of the Effect of Grain Size, Flow Rate and Diffuser Design on the CAWST Biosand Filter Performance
Bibliographic record
Abstract
The effects of grain size, hydraulic loading rate, batch residence time and diffuser design on the performance of the CAWST version 10 biosand filter was investigated. Two types of sand gradations were prepared – fine sand (ES = 0.20, UC = 2.3) and a coarse sand (ES = 0.25, UC = 2.9). The fine sand and coarse grains resulted in initial hydraulic loading rates of 0.3 m3/m2 min and 0.75 m3/m2 min, respectively. Flow restrictions were installed on 2 coarse grain filters such that their initial hydraulic loading rates were 0.3 m3/m2 min. For the range of grain size and flow rates investigated in this paper, coarse grain size leads to lower bacterial removal efficiencies compared to the fine grain size. The addition of flow restrictions on filters with coarse grain size did not result in improvement in bacterial removal efficiencies. Two different diffuser designs were also investigated (hole size of 1/8 inch or 3.2 mm and 0.5 inch or 12.7 mm spacing, hole size of 3/16 inch or 9.5 mm and 0.5 inch or 12.7 mm spacing). These filters were compared to the standard design of CAWST version 10 filter, i.e. control filter, which has a hole size of 1/8 inch (3.2 mm) and 1 inch (25.4 mm) spacing. Diffuser design was found to have an effect on the bacterial removal efficiency. The two diffuser investigated (smaller hole size, tighter spacing) resulted in lower bacterial removal efficiencies, compared to the CAWST specified diffuser design. Batch residence time was found to have a significant effect on bacterial removal. The deficiencies caused by diffuser design, coarse grain size were compensated by a higher batch residence time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".