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Record W1969205929 · doi:10.1002/jctb.1944

Treatment of methanol vapours in biofilters packed with inert materials

2008· article· en· W1969205929 on OpenAlexafffundabout
Antonio Avalos Ramírez, Sandrine Bénard, A. Giroir‐Fendler, J. Peter Jones, Michèle Heitz

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilterVapoursMethanolInertPollutantPulp and paper industryBiomass (ecology)Carbon dioxidePorosityWaste managementEnvironmental engineeringChemistryEnvironmental scienceEnvironmental chemistryOrganic chemistryAgronomy

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Methanol is a major pollutant emitted in Canada. Methanol is toxic to humans and it is associated with environmental problems such as smog generation. Biofiltration is a treatment method of considerable interest for controlling methanol emissions, because of its characteristics: no production of hazardous wastes, low energy consumption and low operating costs. The present study analyzed the effects of porous and non‐porous packing materials, the nitrogen concentration in nutrient solution and the methanol inlet load on biofilter performance and biofilm characteristics. RESULTS: The biofilter packed with porous material presented a removal efficiency up to 95%, which was higher than the 35% removal efficiency with the non‐porous material. Inlet load (IL) influenced the biomass and carbon dioxide production rates. The critical inlet load ( IL crit ) occurred at 80 g m −3 h −1 . The cellular densities of methylotrophs and non‐methylotrophs were affected by all operating variables examined. CONCLUSION: Biofiltration can be applied for controlling methanol emissions with high removal efficiency. The cellular density of methylotrophs is correlated with the performance of the biofilter. Copyright © 2008 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research 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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.016
GPT teacher head0.241
Teacher spread0.226 · 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

Citations17
Published2008
Admission routes3
Has abstractyes

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