Pilot scale evaluation of biofiltration as an innovative pre-treatment for ultrafiltration membranes for drinking water treatment
Bibliographic record
Abstract
Fouling remains one of the major constraints on the use of low pressure membranes in drinking water treatment. Work over the last few years has shown the importance of biopolymers (carbohydrates and protein-like material) as foulants for ultrafiltration (UF) membranes. The purpose of this study was to investigate at pilot scale the use of rapid biofiltration (without prior coagulation or ozone addition) as an innovative pretreatment to reduce fouling of UF membranes. The investigation was carried out on a water with a higher than average DOC and significant temperature variation. The biofilters, each operated at a hydraulic loading of 5 m/h, had empty bed contact times of 5, 10 and 15 minutes. The membrane unit was operated at a flux equivalent to 60 LMH at 20°C. The investigation confirmed the encouraging results obtained in an earlier smaller scale study with essentially the same water. Increased biofiltration contact time (i.e. increased bed depth) led to lower rates of hydraulically irreversible fouling. The initial biofiltration backwash procedure, involving air scour as is common in chemically assisted filtration, led in some cases to an increased rate of membrane fouling immediately after the backwash. An alternative backwashing strategy was developed, however the feasibility of operating with this approach over very long periods of time needs to be confirmed. To assist in full-scale implementation of this “green” and simple pretreatment, the design and operating conditions for the biofilters should be optimized for various types of waters. It is expected that biofiltration pretreatment will be of particular interest for small and/or isolated systems where a higher initial capital cost may be acceptable because of operational simplicity and reduced chemical requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".