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Record W1998071947 · doi:10.1080/09593330.2012.720615

Elimination of nitrogen present in swine manure using a high-efficiency biotrickling filter

2012· article· en· W1998071947 on OpenAlexafffund
Karine Raby, Antonio Avalos Ramírez, Michèle Heitz

Bibliographic record

VenueEnvironmental Technology · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsInstitut de Recherche et de Développement en AgroenvironnementUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaIndustry Canada
KeywordsNitrogenChemistryLeachateNitriteVolumetric flow rateBioreactorPulp and paper industryCarbon dioxideAmmoniumPacked bedEnvironmental engineeringEnvironmental chemistryWaste managementNitrateChromatographyEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Experiments were performed to remove nitrogen as ammonium in biotrickling filters (BTFs) treating synthetic swine manure. Two BTFs packed with polypropylene spheres and ceramic beads were used. BTFs were continuously fed, and leachate obtained was recirculated at different flow rates in the range from 0 to 1.5 L min(-1). When increasing the recirculation flow rate, the carbon dioxide (CO2) production rate increased from 16.5 to 25.6 g CO2 m(-3) h(-1) and nitrogen elimination decreased from 99% to 86% for the polypropylene spheres, whereas for the ceramic beads the CO2 production rate decreased from 20.3 to 15.0 g CO2 m(-3) h(-1) and nitrogen removal from 99% to 90%. The increase of recirculation flow rates also promoted the production of nitrite (NO2(-)) in the leachate. For both packing types, when increasing nitrogen loads from 60 to 240 g N m(-3) day(-1) without recirculation of leachate, the BTFs achieved nitrogen removals of more than 99%. For the same nitrogen loads, nitrogen removal increased from 90% to 99% for the BTF packed with ceramic beads at a recirculation flow rate of 0.6 L min(-1). Operating the BTFs with continuous purge was optimal for biomass production with a maximum level of 71.0 g m(-3) day(-1).

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 score0.021
Threshold uncertainty score0.524

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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