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Record W3092374634 · doi:10.2166/wst.2020.475

Case study of H2S release and transport in a trunk sewer with drops

2020· article· en· W3092374634 on OpenAlexafffundabout
Zhi Yang, David Z. Zhu, Tong Yu, Stephen Edwini-Bonsu, Adam Shypanski, Yanchen Liu

Bibliographic record

VenueWater Science & Technology · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSanitary sewerMass transfer coefficientHydrogen sulphideHydrogen sulfideChemistryEnvironmental engineeringSulfideScrubberEnvironmental chemistryWaste managementSulfurEnvironmental scienceMass transferChromatographyEngineering

Abstract

fetched live from OpenAlex

Abstract Field work was performed to investigate the release of hydrogen sulphide (H2S) and its transport in the sewer trunk with drops in the Bonnie Doon area in Edmonton, Alberta, Canada, in order to develop a proper odor control strategy. The liquid sulfide concentration in the upstream trunk was low (less than 1.0 mg/L), and no H2S gas was detected in the head space under this low concentration. However, high H2S gas concentration was detected in the middle reach of the trunk due to the stripping effect of the three drops (2.7 m, 5.2 m and 2.0 m) along the trunk. The released H2S at drops was then transported in the sewer system and emitted at various locations and caused odor concerns. These drops played an important role in H2S release, and the overall H2S mass transfer coefficient at drops was much higher than that in normal gravity sewers. The overall oxygen and H2S mass transfer coefficient (KLa) was estimated to be around 200 h−1 and 300 h−1 at the first two drops, respectively. Field sampling of biofilm indicates that Desulfomicrobium was identified as the sulfate-reducing bacteria (SRB) responsible for sulfide generation in sewer wall biofilm and Thiobacillus was the only predominant member in manhole wall biofilm contributing to sewer manhole corrosion.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.223
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2020
Admission routes3
Has abstractyes

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