Optimisation and improvement of in-line filtration performance in water treatment for a typical low turbidity source water
Bibliographic record
Abstract
Forest catchments can produce high quality source water with a low turbidity. However, the combination of low turbidity, low organic carbon, and low temperature water conditions presents operating challenges in conventional water treatment processes. In this study, in-line filtration was tested using pilot-scale filter columns, and was found to be an appropriate option to treat a typical low turbidity water originating from the Rocky Mountains near Calgary, Alberta, Canada. When alum and cationic polymer were dosed simultaneously, in-line filtration produced high quality effluent with a turbidity and a particle count value of less than 0.1 NTU and 50 counts/mL, respectively. However, the alum and polymer doses and their ratios played important roles in the filtration efficiency. In general, short filter ripening times (i.e. <15 min) required an alum dose of at least 3 mg/L and an alum to polymer dose ratio of less than 180:1. A longer filter stable period was associated with lower alum and polymer doses, as long as their doses were at least 2 and 0.024 mg/L, respectively, and their dose ratio was maintained in the range of 30:1 to 130:1. The optimal alum and polymer doses were observed to be 3 and 0.072 mg/L, respectively. Filter performance was enhanced when higher alum and polymer doses were used for ripening, and lower doses were applied during the stable filtration period. In addition, in-line filtration resulted in the reduction of microspheres by 3.6 logs under the tested water conditions. Hence, a similar removal efficiency is anticipated for Cryptosporidium.
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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".