Chemically enhanced backwashing for NOM removal of ceramic ultrafiltration membranes using conventional cleaning chemicals with a surfactant
Bibliographic record
Abstract
Membrane filtration has developed into a robust water treatment step.However, limited research has been conducted on new membrane cleaning solutions.Conventional membrane cleaners such as NaOCl are known to form DBPs with organic matter and damage polymeric membranes at high concentrations.Our research examines the use of surfactant (SDS) in combination with conventional membrane cleaners (NaOCl and NaOH) to clean a ceramic UF membrane as chemically enhanced backwash (CEB).Existing research primarily looked at the combination of surfactants with NaOH for long duration cleaning.Limited research has also been conducted on the combination of surfactants with high NaOCl concentrations.Initial tests showed the addition of SDS significantly reduced the surface tension of various CEB solutions.Subsequent fouling and cleaning studies demonstrated better cleaning efficiency when CEB contained SDS.In general, the addition of SDS provided some improvement to backwash fouling control although more research is needed in this area.iii Acknowledgement Firstly, I would like to thank my supervisor, Dr. Onita D. Basu for giving me the opportunity to do this research project.This research experience helped me learn more about myself
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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 | 0.000 | 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) | 0.001 | 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".