Surfactant-Enhanced Cleaning Solutions for Ceramic Membranes: A Comparative Study on Humic Acid and BSA Fouling
Bibliographic record
Abstract
Control of natural organic matter (NOM) reversible and irreversible fouling with ceramic membranes for drinking water applications with chemically enhanced backwash (CEB) protocols is limited. This research examines the efficiency of various chemical combinations with non-ionic surfactants to control the NOM fouling caused by humic acid (HA) and protein foulants. Two commercially available non-ionic surfactants, Tween 80 and Triton X100, combined with conventional cleaning solutions, were analyzed with respect to membrane fouling and cleaning using the resistance in series (RIS) model, membrane permeability, carbon mass balance, and contact angle measurements. The results demonstrated that in all cases, CEB outperformed hydraulic backwashing; in addition, the inclusion of surfactants demonstrated enhanced the fouling control with protein foulants more than humic acid. The transmembrane pressure (TMP) with surfactant CEB was controlled to within a range of 83-105 kPa compared to hydraulic backwash at approx. 128 kPa for HA and BSA. The carbon mass balance analysis indicates that Tween 80 surfactant-based CEB demonstrated effective fouling control, leaving only 20% irreversible fouling with HA and 30% with BSA while the hydraulic backwash resulted in 57% irreversible fouling of carbon on the membrane for HA and BSA.
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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 | 0.001 |
| 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 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".