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Record W2901749842 · doi:10.1016/j.btre.2018.e00293

Mathematical modelling of in-situ microaerobic desulfurization of biogas from sewage sludge digestion

2018· article· en· W2901749842 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiotechnology Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Alberta
FundersUniversidad de Valladolid
KeywordsAnaerobic digestionBiogasSewage sludgeHydrogen sulfideIn situAnaerobic exerciseEnvironmental sciencePulp and paper industryFlue-gas desulfurizationDigestion (alchemy)ChemistryBiochemical engineeringWaste managementSewageEnvironmental engineeringEngineeringMethaneBiologySulfurChromatography

Abstract

fetched live from OpenAlex

Microaeration can be used to cost-effectively remove in-situ H2S from the biogas generated in anaerobic digesters. This study is aimed at developing and validating an extension of the Anaerobic Digestion Model n°1 capable of incorporating the main phenomena which occurs during microaeration. This innovative model was implemented and tested with data from a pilot scale digester microaerated for ∼ 200 d. The results showed that despite the model’s initial ability to predict the digester’s behavior, its predicted performance was improved by calibrating the most influential parameters. The model’s prediction potential was largely enhanced by adding retention parameters that account for the activity of sulfide oxidizing bacteria retained inside the anaerobic digester, which have been consistently shown to be responsible for a large share of the H2S removed.

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.

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 score0.090
Threshold uncertainty score0.457

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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