Wastewater Disinfection Using Peracids: Simulation of System Performance and Process Control Alternatives Using Batch Inactivation Kinetics from a 10-Plant North American Study
Bibliographic record
Abstract
Wastewater Disinfection Using Peracids: Simulation of System Performance and Process Control Alternatives Using Batch Inactivation Kinetics from a 10-Plant North American StudyAbstractOver the last two decades, peracetic acid has gained popularity for municipal wastewater disinfection, as reported by several investigators who have conclusively demonstrated good antimicrobial properties against a wide range of microorganism models including bacteria. More recently, performic acid (PFA) has been studied as a substitute for PAA. In this study, we present recent disinfection and demand/decay and microbial inactivation kinetic results from ten North American wastewater treatment plants for PAA, PFA and total chlorine. These results are utilized in a simulated contact chamber where we consider five different dose control strategies; from simple constant disinfectant dosing to advance controls where demand and decay are measured online and adjustments to the dosing are made in real time. System performance when compared to five control strategies anticipate a lesser demand for a disinfectant for advanced control strategies at the expense of higher geomean microbial counts at the outlet. However, it is observed that using an advanced control strategy for chemical disinfection results in a steady performance and requires less quenching to meet regulatory compliance.This paper was presented at WEFTEC 2023 in Chicago, IL.SpeakerLawryshyn, YuriPresentation time16:30:0017:00:00Session time15:30:0017:00:00SessionAdvancements and Optimization with Chlorine, Peracetic Acid, and Performic Acid DisinfectionSession locationRoom S405 - Level 4TopicDisinfection and Public Health, Intermediate Level, Municipal Wastewater Treatment Design, Research and InnovationTopicDisinfection and Public Health, Intermediate Level, Municipal Wastewater Treatment Design, Research and InnovationAuthor(s)Lawryshyn, YuriAuthor(s)Y. Lawryshyn 1; L. Tikariha 1 ; J. Norton. John 2; J. Da Silva 3; K. Bell 4; D. Santoro 5; Y. Lawryshyn 1;Author affiliation(s)University of Toronto, Ontario, CANADA 1; University of Toronto, Ontario, CANADA 1 ; Great Lakes Water Authority 2; Brown and Caldwell 3; Brown and Caldwell 4; USP Technologies 5; University of Toronto 1;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159016Volume / Issue Content sourceWEFTECCopyright2023Word count23
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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 | 0.000 | 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 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".