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Record W1543690838 · doi:10.1002/jctb.4162

Kinetics of simultaneous methane and toluene biofiltration in an inert packed bed

2013· article· en· W1543690838 on OpenAlexafffund
Camille Ménard, Antonio Avalos Ramírez, Michèle Heitz

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCentre National en Électrochimie et en Technologies EnvironnementalesUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaSiemens
KeywordsBiofilterTolueneMethaneChemistryBiogasBiodegradationSaturation (graph theory)Anaerobic oxidation of methaneEnvironmental chemistryPacked bedChromatographyWaste managementOrganic chemistryEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Biofiltration of methane is of particular interest to contribute to limiting the greenhouse gas effect of biogas emissions from landfills. The complexity of the biogas mixture from landfills has underlined the importance of the presence of non‐methane organic compounds. The aim of this study was to determine the effect of toluene on the microkinetic and macrokinetic parameters of methane biodegradation using an inorganic filter bed. RESULTS Two concentrations of toluene were tested, 0.7 and 3.4 gC m −3 , and compared with the case of methane biofiltration alone. The specific growth rates of methane decreased from 0.793 to 0.574 to 0.278 d −1 when the toluene concentration was increased from 0 to 0.7 to 3.4 gC m −3 , respectively. The maximum elimination capacity of methane decreased from 39.4 to 5.6 gC m −3 h −1 when toluene concentration was increased from 0 to 3.4 gC m −3 . The half‐saturation constants decreased from 4.6 to 1.6 gC m −3 and from 4.6 to 0.7 gC m −3 , respectively. CONCLUSIONS Results show that an inhibition occurred on methane biodegradation when toluene was introduced into the biofilter. © 2013 Society of Chemical Industry

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 score0.037
Threshold uncertainty score0.584

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.229
Teacher spread0.223 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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