Pretreatment Alternatives for the Ultrafiltration of Algae Laden Water
Bibliographic record
Abstract
The excessive growth of algae from eutrophication as a result of human activities is one of the most serious problems in drinking water treatment. During algae blooms, the excessive release of algae organic matter (AOM) changes the organic matter distributions in surface waters causing severe membrane fouling. Comparisons of the fouling of UF membranes treating surface waters with different natural organic matter (NOM) distributions were done to identify the organic fractions that cause membrane fouling. The waters tested were prepared by combining NOM from Lake Ontario with AOM from four algae species (Chlorella sp., Microcystis aeruginosa, Merismopedia sp., and Cyclotella sp.) commonly found in freshwater. Liquid chromatography-organic carbon detector (LC-OCD) and fluorescence excitation-emission matrix (FEEM) analytical techniques were used to characterize the organic fractions. The membrane fouling was attributed to the hydrophilic fraction of NOM, especially to biopolymer-polysaccharides and proteins as characterized via LC-OCD, and predominant FEEM responses from protein-like and SMP-like substances. The fouling behaviors of the organic fractions were influenced by the addition of natural occurring divalent and metal cations at different doses and pHs. The membrane treating waters with high content of polysaccharides and proteins experienced the highest membrane fouling and membrane resistances. The increase in membrane fouling was attributed to their absorption within the membranes. The addition of divalent cations decreased the membrane fouling as a result of their complexation with AOM. Higher membrane fouling was observed as pH decreased. At pH 6, the membrane experienced the lowest organic matter retention, especially biopolymers and protein-like substances. This was attributable to charge neutralization and reduced electrostatic attraction with the membranes. At pH 8.0, the increase in electrostatic repulsion facilitated the formation of looser layers that led to higher retention of biopolymers and protein-like substances. The fouling behaviors of the organic fractions were compared using sedimentation and dissolved air flotation (DAF) as pre-treatment and influenced by the addition of aluminum and iron-based coagulants. Sedimentation produced better effluent quality than DAF. Membrane fouling decreased with the addition of coagulants, especially aluminum based. Waters with higher biopolymer contents were more susceptible to coagulation achieving higher organic matter removal.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".