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Record W2081512040 · doi:10.1080/10934529.2012.667327

Biofiltration of air polluted with methane at concentration levels similar to swine slurry emissions: Influence of ammonium concentration

2012· article· en· W2081512040 on OpenAlexafffund
Marc Veillette, Antonio Avalos Ramírez, Michèle Heitz

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsBiofilterSlurryEnvironmental chemistryMethaneAmmoniumChemistryEnvironmental scienceMethane emissionsEnvironmental engineeringPulp and paper industryWaste managementOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

An evaluation of the effect of ammonium on the performance of two up-flow inorganic packed bed biofilters treating methane was conducted. The air flow rate was set to 3.0 L min(-1) for an empty bed residence time of 6.0 min. The biofilter was fed with a methane concentration of 0.30% (v/v). The ammonium concentration in the nutrient solution was increased by small increments (from 0.01 to 0.025 gN-NH(4) (+) L(-1)) for one biofilter and by large increments of 0.05 gN-NH(4) (+) L(-1) in the other biofilter. The total concentration of nitrogen was kept constant at 0.5 gN-NH(4) (+) L(-1) throughout the experiment by balancing ammonium with nitrate. For both biofilters, the methane elimination capacity, carbon dioxide production, nitrogen bed retention and biomass content decreased with the ammonium concentration in the nutrient solution. The biofilter with smaller ammonium increments featured a higher elimination capacity and carbon dioxide production rate, which varied from 4.9 to 14.3 g m(-3) h(-1) and from 11.5 to 30 g m(-3) h(-1), respectively. Denitrification was observed as some values of the nitrate production rate were negative for ammonium concentrations below 0.2 gN-NH(4) (+) L(-1). A Michalelis-Menten-type model fitted the ammonium elimination rate and the nitrate production rate.

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)0.001
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 score0.130
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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

Citations10
Published2012
Admission routes2
Has abstractyes

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