Effect of granular activated carbon adsorption on mitigating microfiltration membrane fouling by algal organic matter
Bibliographic record
Abstract
Abstract Algal blooms can seriously affect the operation of water treatment processes including low-pressure (micro- and ultrafiltration) and high-pressure (nanofiltration and reverse osmosis) membranes mainly due to the accumulation of algae-derived organic matter (AOM). This study investigated the effect of granular activated carbon (GAC) pretreatment on PVDF microfiltration performance for the removal of AOM. Dissolved organic matter (DOM) solution of commercial humic acid, extra- and intracellular organic matter from two species of algae, and Cyanobacteria were used for the investigation of the fouling potential of the membrane. A comparison study of different DOM removal and fouling behaviors of microfiltration (MF) after GAC adsorption as pretreatment was evaluated under variable GAC dosage and solution pH. Almost 15–20% improvement in flux and decline in irreversible fouling occurred due to the pretreatment using 1.0 g/L of GAC for an hour. The intracellular material caused higher membrane fouling than humic acid due to the hydrophilic nature of the AOM. Membrane fouling and decline in flux increased with increasing pH in the range of 5.0–8.0. The comparison results might help to provide insights into the real challenge of dealing with the treatment of algal-laden water.
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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".