Effects of total suspended solids, particle size, and effluent temperature on the kinetics of peracetic acid decomposition in municipal wastewater
Bibliographic record
Abstract
Abstract In this study, the influence of total suspended solids (TSS) and particle size as well as effluent temperature on peracetic acid (PAA) decomposition kinetics in municipal wastewater was investigated. PAA decomposition was best described following second order kinetics in primary effluent (PE) and first order kinetics in secondary effluent (SE) samples. For synthetic samples prepared by varying TSS levels, PAA demand increased on average by about 0.042 mg/L in PE and 0.034 mg/L in SE for every 10 mg/L increase in TSS. Similarly, the PAA decay rate constant in these samples increased at a rate of 0.0014 L/mg.min and 0.00039 min−1, respectively, per 10 mg/L TSS. To examine the effect of particle size, synthetic samples with narrow size fractions (20–45, 45–75, and 75–90 μm) were prepared. It was found that samples with smaller particle size fractions had a greater PAA demand and decay rate constant. Effluent temperature also enhanced the PAA decomposition rate with the calculated activation energies for PE and SE samples being 29,980 J/mol and 34,860 J/mol, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".