Decrease of Time for Pathogen Inactivation in Alkaline Disinfection Systems Using Pressure
Bibliographic record
Abstract
From field studies conducted by Tulane University (New Orleans, Louisiana), efficiency of advanced alkaline disinfection in closed systems was found to depend on ammonia concentration, pH, exposure time, temperature, total solids content, pretreatment storage time, and mixing effectiveness. In this study of a closed alkaline system, an additional pathogen stressor pressure was tested. The effect of the alkaline dosing has been assessed for dewatered raw and aerobically and anaerobically digested municipal sludge cake that produce un-ionized ammonia at concentrations of 0.05 to 2% on a dry-weight basis. Inactivation of Ascaris suum eggs increased from 50 to 99% as the temperature was increased from 40 to 55 degrees C, thus achieving Class A levels. The systems studied were compared with an alkaline process operated under open conditions, which limited the concentrations of ammonia available because of Henry's Law. Under a closed pressurized system, the effect of un-ionized ammonia was greatly increased, and the resulting time required for inactivation was reduced from hours or days to minutes. In the next few years, it is expected that alkaline disinfection of biosolids will be optimized in relation to the factors stated above, at much lower doses of the alkaline agents. The closed-system alkaline processes that will be developed will be more energy-efficient, cost-effective, and have full control of potential odorous emissions.
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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) | 0.000 | 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) | 0.002 | 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".