Chemical cleaning protocols for passive gravity driven membrane filtration
Bibliographic record
Abstract
Passive Gravity Driven Membrane Filtration (PGDMF) systems were specifically developed to ensure long-term access to safe drinking water in small, remote, and marginalized communities. Chemical cleaning protocols exist for conventional Ultrafiltration (UF) filtration systems to effectively remove physically irreversible foulants. The same protocols have been applied to PGDMF systems, but with limited success. Therefore, modified chemical cleaning protocols are needed for PGDMF systems. The present study systematically assessed the efficacy of different chemical cleaning protocols using sodium hypochlorite as the only chemical cleaning agent. The study utilized PVDF hollow fiber membranes harvested from the full-scale PGDMF system that provides drinking water to the Kleekhoot Reserve of the Hupacasath First Nation in British Columbia, Canada. Bench-scale chemical cleaning trials were carried out with a total of 162 chemical soak experiments (including duplicates) on harvested fibers, for different combinations of concentration of sodium hypochlorite, soaking temperature, and soaking (i.e., exposure) duration. The results indicated that the recovery in resistance due to fouling during chemical cleaning with sodium hypochlorite was governed by two rates: an initial rapid rate of recovery and a subsequent slow rate of recovery. Despite differing rates, both reactions exhibited a similar extent of recovery. It was concluded that effective recovery in the resistance due to fouling can be achieved using sodium hypochlorite as the only chemical cleaning agent. The required duration of chemical cleaning increases with higher targeted recoveries in resistance due to fouling and is also greater at lower temperatures and concentrations of sodium hypochlorite. An approach was proposed to establish chemical cleaning protocols based on site-specific conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".