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Record W4407510385 · doi:10.1016/j.psep.2025.106911

Micro-aeration for hydrogen sulfide reduction in full-scale anaerobic digesters with limited headspace: Performance and sulfide reduction kinetics

2025· article· en· W4407510385 on OpenAlexaff
Ali Khadir, George Nakhla, Renisha Karki, Lutgarde Raskin, Christopher Muller, Karla Guevarra, Amanda Summers, Laurie Pierce, Parisa Shahbaz, Kati Bell, Steven J. Skerlos, Embrey Bronstad

Bibliographic record

VenueProcess Safety and Environmental Protection · 2025
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsWestern University
Fundersnot available
KeywordsHydrogen sulfideSulfideReduction (mathematics)AerationKineticsAnaerobic exerciseChemistryWaste managementEnvironmental sciencePulp and paper industryEngineeringSulfurMathematicsMedicineOrganic chemistry

Abstract

fetched live from OpenAlex

A full-scale trial of micro-aeration was conducted at a municipal wastewater plant using two anaerobic digesters with limited headspace, one as a control and one as a test. A reduction of approximately 13 %–16 % in hydrogen sulfide biogas concentrations was observed in the test digester. Microbial community analyses were also conducted at steady-state conditions to determine differences between the microbial ecology of the test and the control digesters. Although sulfide removal typically occurs in the headspace of a micro-aerated digester, the data generated by this pilot study developed a new approach to quantify the contribution of liquid phase sulfide oxidizing microbes (SOM).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.187
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2025
Admission routes1
Has abstractyes

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