Impact of electrocoagulation of soluble microbial products on membrane fouling at different volatile suspended solids’ concentrations
Bibliographic record
Abstract
This research had two objectives: (1) to study the combined effect of volatile suspended solids (VSS) and soluble microbial product (SMP) on membrane fouling in an attempt to explain the discrepancies of previous studies and (2) to investigate the feasibility of reducing SMP impact on membrane fouling rate by electrocoagulation. Electrocoagulation successfully removed up to 55% and 90% of protein and polysaccharides, respectively, which resulted in a substantial reduction of membrane fouling rate (four times less). The results showed that at a comparable VSS concentration, membrane fouling increased with an increase in SMP. For example, for the same magnitude of VSS, membrane fouling rate was four times higher as the concentration of SMP tripled. Higher VSS concentrations were not directly responsible for higher fouling rates unless there was an increase in the SMP concentration. It was concluded that the correlation of membrane fouling with VSS alone is misleading unless accompanied with SMP concentration. Statistical analysis demonstrated that VSS impact on membrane fouling was not significant when it was considered as a single independent variable. The most accurate prediction of the membrane fouling was built by multiple regression model based on a quadratic VSS and linear SMP as independent variables.
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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".