Comparing Sedimentation, Flotation, and In-Line Pretreatment for Low-Pressure Membrane Fouling Reduction
Bibliographic record
Abstract
Feed water pretreatment commonly is required for low-pressure membrane technologies employed in drinking water treatment applications to reduce membrane fouling and create stable operating conditions. Comparatively few studies have investigated coagulation–flocculation–dissolved air flotation (CF-DAF) pretreatment for drinking water applications, and none have compared CF-DAF, coagulation–flocculation–sedimentation (CF-S), and in-line coagulation (CF-IN) pretreatments using the same water. This study compared these three pretreatments for the filtration of a high dissolved organic carbon (DOC), high hydrophobic (HPO) surface water using a hydrophilic polyvinylidene fluoride (h-PVDF) ultrafiltration (UF) fiber. Multiday filtration tests were carried out using an automated bench-scale testing system operated in an outside-in configuration. CF-S and CF-DAF were found to be equally effective at mitigating membrane fouling, although CF-DAF pretreated water had a lower residual DOC and the greatest removal of UV254 absorbent organics. Compared with CF-DAF and CF-S, CF-IN pretreatment resulted in higher levels of total and irreversible fouling regardless of the applied coagulant dose. For all the pretreatments studied, irreversible membrane fouling was found to be strongly dependent on both the hydrophobicity of the feed water [in terms of specific UV absorbance (SUVA)] and the concentration of the 5–10-kDa DOC fraction, suggesting that the HPO humic organics were the principal foulant for this membrane–water combination. CF-IN pretreatment performance also was strongly impacted by the feed water zeta potential, suggesting that the characteristics of the formed flocculant particles are critical to the fouling behavior of the hybrid CF-IN-UF system.
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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".