Evaluation of adsorption and coagulation as membrane pretreatment steps for the removal of organic material and disinfection-by-product precursors
Bibliographic record
Abstract
Adsorption using powdered activated carbon and coagulation using polyaluminium chloride or aluminum sulphate were investigated as pretreatment steps prior to membrane filtration to remove organic material and trihalomethane (THM) precursors contained in a raw drinking water source. Ultrafiltration and microfiltration membrane treatment alone could not effectively and consistently remove organic material, measured as total organic carbon, and THM precursors, measured as chloroform formation potential, contained in the raw water. Coagulation, prior to membrane treatment, significantly improved the removal of organic material and THM precursors contained in the raw water. Microfiltration membrane treatment, with pre-coagulation, consistently removed approximately 75% of the organic material and the THM precursors contained in the raw water. A coagulant concentration of approximately 0.3 mg/L, as Al, was sufficient to achieve this high removal efficiency. Adsorption, prior to membrane treatment, did not significantly improve the removal of organic material or THM precursors. Key words: adsorption, chloroform, coagulation, disinfection-by-products, drinking water treatment, microfiltration, trihalomethanes, ultrafiltration.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".