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Record W2518231191 · doi:10.1021/ie0011270

Toluene Removal by Biofiltration:  Influence of the Nitrogen Concentration on Operational Parameters

2001· article· en· W2518231191 on OpenAlexafffund
Marie‐Caroline Delhoménie, Louise Bibeau, Julie Gendron, Ryszard Brzeziński, Michèle Heitz

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2001
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilterNitrogenTolueneCompostBiodegradationDegradation (telecommunications)ChemistryVolumetric flow rateInletFilter (signal processing)NutrientEnvironmental chemistryChromatographyPulp and paper industryEnvironmental scienceEnvironmental engineeringWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

The study presented in this paper dealt with the operation of a laboratory-scale upflow biofilter, packed with compost-based filter material. The airborne contaminant studied was toluene, maintained at a constant inlet concentration of 1.7 g·m -3 . The input air was conveyed upward through the filter column at a flow rate of 1 m 3 ·h -1 . The objective of this work was the study of the impact of increasing concentrations of nitrogen contained in the nutrients solution and, hence, the establishment of a new correlation between this parameter and the overall degradation performance. Depending on the nitrogen concentration employed, two biodegradation regimes have been identified. Over the optimal range of nitrogen concentrations [2.0−8.0 g of N·L -1 ], the maximum level of elimination capacity achieved was ≃100 g·m -3 ·h -1 . This value is in line with theoretical considerations that suggest that an optimal nitrogen concentration of ≃2.6 g of N·L -1 is required to achieve the same performance (100 g·m -3 ·h -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)0.002
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 score0.063
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.307
Teacher spread0.244 · 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

Citations35
Published2001
Admission routes2
Has abstractyes

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