Chemical disinfection of secondary municipal wastewater effluents: Optimizing CT dose and tailing effects through high‐intensity mixing
Bibliographic record
Abstract
Abstract This paper investigates the impact of average velocity gradient and mixing effects on secondary wastewater coliform inactivation kinetics using an innovative in‐line treatment technology based on sodium hypochlorite as disinfecting agent. Experiments included both laboratory batch kinetic studies (as reference) as well as bench‐scale pilot tests. The laboratory studies were carried out using a magnetically stirred vessel to simulate low‐mixing conditions (Ḡ ≈ 1000 s −1 at 1 atm), while the bench‐scale pilot tests employed a flow‐through system consisting of two centrifugal pumps in series to simulate high average velocity gradients and intense mixing conditions (Ḡ ≈ 10,000 s −1 at 1.5 atm). In both cases, disinfectant demand and decay models for sodium hypochlorite were fitted against observed data using various expressions corresponding to different kinetic orders and subsequently incorporated into fecal inactivation kinetics via their integral CT expression. Experimental results showed a very remarkable and significant influence of high velocity gradient and mixing intensity on disinfection efficiency. While conventional batch kinetics indicated a 3‐log reduction in fecal coliforms at concentration‐time integral product (CT) of 16 (mg·min·L −1 ), less than 1/10th of the CT dose (under comparable process conditions) were needed in the case of advanced disinfection with high average velocity gradient and mixing intensity. Using the experimental data collected in this study, a novel inactivation model was developed that uniquely incorporates the average velocity gradient Ḡ as explicitly kinetic parameter, enabling precise prediction of CT required for various mixing conditions to meet specific microbial treatment targets. To achieve an effluent total coliform concentration of 10 CFU per 100 mL, a CT of 48.5 mg·min·L −1 was required at a mixing intensity of Ḡ = 762 s −1 , while only 0.82 mg·min·L −1 was needed at Ḡ = 18,158 s −1 . Inactivation tailing was drastically reduced under high‐mixing conditions by enhancing disinfectant penetration in the flocs shielding particle‐associated coliforms. Furthermore, disinfection by‐product (DBP) screening tests confirmed that enhanced inactivation under high‐mixing conditions was achieved while also maintaining regulated DBP levels across all CT values. This integration of mixing effects in microbial inactivation kinetics marks a significant advancement over traditional disinfection design frameworks allowing the disinfection community to access a more refined approach for sizing and validation purposes. PRACTITIONER POINTS Particle‐associated coliforms are inactivated by hypochlorite under high mixing. A 3‐log reduction of coliforms observed at more than 30 times lower CT under high mixing. High mixing and mild pressure can reduce chlorine dose and contact time significantly. Tailing effects are well mitigated by high mixing combined with sodium hypochlorite. An inactivation model for coliform bacteria accounting for mixing intensity is proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".