Optimizing alum coagulation for turbidity, organics, and residual Al reductions
Bibliographic record
Abstract
Increases in residual dissolved Al from alum coagulation associated with low water temperatures should be minimised to avoid problems in the distribution mains and as a precautionary approach to possible health effects of Al. Temperature-controlled jar-tests (0.1 to 17.0°C) were used to evaluate optimisation of a plant using alum coagulation at pH 6.0 followed by activated silicate addition. pH adjustment was assessed during coagulation and flocculation (i.e. before and after activated silicate) in order to control residual Al by precipitation without affecting turbidity and natural organic matter (NOM) reductions. The effects on NOM reduction were marginal until pH 6.4 in all conditions tested. Turbidity reductions by sedimentation considerably worsened when increasing pH, especially at the lowest temperature. Experimental conditions to eliminate this negative effect were found to be by increasing the pH after silicate addition (from the coagulation pH of 6.0 to between 6.1 and 6.3). By pH adjustments after silicate addition, up to 90% decrease in dissolved Al could be obtained at pH ∼6.8 (from 180 to ∼20 μg/L) at low temperatures. At this pH level, however, turbidity reduction reached minimal values (10–20%) while NOM reduction was clearly affected, indicating partial NOM re-dissolution. Slight pH adjustments of coagulation pH (up to 6.1) or flocculation pH (up to 6.3—after silicate addition) promised significant dissolved Al minimization (down to ∼50 μg/L) without compromising turbidity or NOM reductions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".