MétaCan
Menu
Back to cohort
Record W4402053982 · doi:10.1061/joeedu.eeeng-7691

Field Study of Generation and Emission of Hydrogen Sulfide in a Sanitary Sewer Network Downstream of a Long Forcemain

2024· article· en· W4402053982 on OpenAlexaffabout
Letian Sun, Xiaojie Shi, Tong Yu, David Z. Zhu, Adam Shypanski

Bibliographic record

VenueJournal of Environmental Engineering · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHydrogen sulfideSanitary sewerDownstream (manufacturing)Environmental scienceEnvironmental engineeringField (mathematics)Environmental chemistryWaste managementChemistryEngineeringSulfur

Abstract

fetched live from OpenAlex

Fieldwork was carried out in the western area of Edmonton, Alberta, Canada to assess the H2S contribution of a long force main and to investigate the source of H2S generation in a complex sewer network. The study sewer trunk has a length of 10 km, a diameter of up to 1.95 m, and flow rate of up to 0.6 m3/s. In the upstream, there is a 5.6 km forcemain, and the pump station operation can cause a sudden increase of H2S in the sewer air of the discharge manholes to reach 500 ppm. Wastewater samples were collected in 14 manholes in the sewer network and analyzed for sulfide and other relevant parameters. The maximum values of sulfide were 9.7 mg S/L and pH remained mostly neutral. For predicting the sulfide generation rate, an empirical model was applied with readily biodegradable organic matter. Then the emission of H2S in gravity pipes was investigated, and it was found over 90% of H2S stayed in the liquid phase when wastewater flowed in gravity sewer pipes. Finally, the mass transfer in a drop structure of 8 m was investigated. The liquid phase H2S concentration in the upstream was 2.6 times that of the downstream, and about 62% of the H2S was released in this drop structure indicating the significant emission of H2S in drop structures.

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 score0.149
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Environmental EngineeringSame topicOdor and Emission Control TechnologiesFrench-language works237,207