Biopolymer removal in full-scale conventional and advanced drinking water treatment trains at two large adjacent plants
Bibliographic record
Abstract
The performance of conventional and advanced processes with regard to the reduction of the biopolymer fraction of natural organic matter (NOM) at two large adjacent full-scale drinking water treatment plants was compared and evaluated. Both plants were fed with the same surface water source, however, they differed in configuration and type of coagulant used. The biopolymer fraction, although of critical importance for low-pressure membrane fouling, is only a relatively small percentage of the overall NOM. Therefore, to provide context and comparison, the removal of humic substances, the largest NOM fraction, was also investigated. It was observed that the plant using aluminum sulfate slightly outperformed the one dosing polyaluminum chloride for biopolymer and humic substance removal when the coagulants were dosed at average concentrations of 0.68 ± 0.23 mg Al/L and 1.10 ± 0.63 mg Al/L, respectively. Under the conditions investigated, coagulation, flocculation, and sedimentation may be a better pre-treatment option for ultrafiltration membranes in terms of biopolymer removal compared to ozonation and biofiltration. However, as confirmed by Liquid Chromatography-Organic Carbon Detection analysis, biopolymer removal through biofiltration with prior ozonation was less than expected, suggesting that the process pairing was not optimized. This research illustrates the value of biopolymer quantification with respect to assessing its impact on membrane fouling.
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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 | 0.001 | 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".