Membrane fouling and its control in drinking water membrane filtration process
Bibliographic record
Abstract
Treatment of surface water in the presence of natural organic matters (NOM) becomes a challenging issue to meet stringent rules of Safe Drinking Water Act (SDWA). Ultrafiltration (UF) membrane is emerging as an efficient technology for the purpose of potable water production. However, membrane fouling, ageing and chemical cleaning affect its performance and properties. \nThe effects of ageing and chemical cleaning on performance and properties of the membrane were studied using UF membrane from full scale drinking water membrane filtration plant and simulated chemical cleaning sequences in laboratory. Organic and inorganic foulants, and membrane properties such as tensile strength, membrane morphology and surface functional groups were characterized using various analytical tools. The results from simulated chemical cleaning experiments were consistent with those from a full-scale plant, in terms of the effects of chemical cleaning on membrane properties. The results show that membrane ageing deteriorated the tensile strength and membrane integrity, and led to accumulation of foulants. Hypochlorite cleaning resulted in a decrease in membrane tensile strength, while citric acid cleaning had limited effect on membrane tensile strength. The decrease in membrane tensile strength correlated to a decrease in intensity of functional groups measured by FTIR. The results suggest that hypochlorite concentration and cleaning time should be minimized to reduce their impacts on membrane properties. \nAdditionally, membrane cleaning strategies (cleaning agents? concentration, cleaning time, pH, backwash frequency, and production time) currently used in Bare Point Water Treatment Plant were studied using a ZW-1000 pilot scale plant. The membrane performance in terms of permeability recovery was assessed using the recorded data; and organic and inorganic foulants were analyzed using Total Organic Carbon analyser (TOC) and Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES). In sodium hypochlorite (NaClO) cleaning, lower concentrations combined with longer soak time achieved higher permeability recovery, with TOC results indicating that the major foulants responsible for permeability decrease were organic. Similarly, the results of citric acid cleaning suggest that lower pH was more effective in permeability recovery. \nFurthermore, the effect of production cycle or backwash frequency on the membrane performance was also studied to optimize water recovery; the results revealed that the membrane performances, fouling rate in terms of rate of change of TMP, recovery (%), and organic fouling depended on permeate cycle length or back wash frequency. This research concludes with the hypothesis that membrane fouling and ageing deteriorate membrane performance, whereas chemical cleaning agent (NaClO) enhances membrane performance and properties, respectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".