Catalytic Destruction of Chloramine to Nitrogen Using Chlorination and Activated Carbon—Case Study
Bibliographic record
Abstract
The paper presents the results of laboratory and pilot studies on the removal of chloramine from potable water using chlorination with a less-than-breakpoint dosage of chlorine, followed by treatment with catalytic activated carbon. The effect of the chlorine-to-nitrogen ratio, temperature, and carbon contact time were investigated to optimize conditions for chloramines removal and minimize the production of ammonia. Results demonstrated that prechlorination of water, followed by treatment with catalytic activated carbon, can degrade monochloramine to nitrogen gas as a main product. For all chlorine-to-ammonia ratios studied, the observed rates of monochloramine removal were higher at a temperature of 20 degrees C than they were at 5 degrees C. Generation of ammonia was slightly higher at the lower temperature. However, at both temperatures, practically all monochloramine was destroyed, and only insignificant amounts of ammonia were formed when a chlorine-to-ammonia ratio of 7:1 was applied. The described method is simple and cost-effective, because it eliminates the requirement of removal of ammonia, typically formed during the treatment of chloramines with activated carbon.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".