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Record W2017465418 · doi:10.1002/jctb.1644

Biotrickling filtration of air contaminated with ethanol

2007· article· en· W2017465418 on OpenAlexafffund
Antonio Avalos Ramírez, J. Peter Jones, Michèle Heitz

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInletFiltration (mathematics)Residence time (fluid dynamics)ChemistryPulp and paper industryEthanolEnvironmental engineeringBiomass (ecology)Environmental scienceEnvironmental chemistryEcologyMathematicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract A biotrickling filter (BTF) for treating high ethanol loads was operated for one year and the effect of operating conditions was studied. The BTF was operated in a range of ethanol inlet concentrations of 0.2–15.0 g m −3 and at three different residence times (30, 65 and 130 s). The experiments show that removal efficiency decreased with increasing ethanol inlet concentration and decreasing air residence time. Removal efficiency varied in the range of 60–100%. A maximum elimination capacity of 970 g m −3 h −1 was obtained for an inlet load of 1610 g m −3 h −1 . At a constant residence time, the carbon dioxide (CO 2 ) production rate varied with ethanol inlet concentration. BTF presented the maximum CO 2 production rate in the range of inlet concentration of 3.0–7.0 g m −3 . Two strategies for controlling biomass accumulation were applied: one consisted in periodical washing; the other combined periodical washing with nutrient starvation by consuming less water and energy. Both strategies led to maintaining the BTF stable, with high adaptability and reproducibility. Copyright © 2007 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.051
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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.

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

Citations20
Published2007
Admission routes2
Has abstractyes

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