The removal of anionic surfactants from water in coagulation process
Bibliographic record
Abstract
This paper presents the results of a laboratory study on the effectiveness of the coagulation process in removing surfactants from water. The application of traditional coagulants (aluminium sulfate and iron chlorides) has not brought satisfactory results, the reduction in anionic surfactant (AS) content reached 7.6% and 10%, respectively. Adding cationic polyelectrolyte (Zetag-50) increased the removal efficiency to 24%. Coagulation using a polyelectrolyte alone proved to be more efficient, the reduction in surfactant content fluctuated at a level of about 50%. Complete surfactant removal was obtained when powdered activated carbon was added 5 minutes before the basic coagulant to the coagulation process. The efficiency of surfactant coagulation also increased after the application of powdered clinoptilolite, but to a smaller degree. Then the removal of AS was found to be improved by dosing powdered clinoptilolite simultaneously or with short delay after the addition of the basic coagulant.
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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) | 0.000 | 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".