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Record W2004798114 · doi:10.1002/jctb.835

Mathematical modeling of gas‐phase biofilter performance

2003· article· en· W2004798114 on OpenAlexafffund
Hasnaa Jorio, G. Payre, Michèle Heitz

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilterSorptionOxygenCarbon dioxideBiodegradationLimiting oxygen concentrationStyreneChemistryFilter (signal processing)Process (computing)Steady state (chemistry)Chemical engineeringEnvironmental chemistryEnvironmental engineeringEnvironmental scienceOrganic chemistryAdsorptionComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In the present paper, a new mathematical model describing the physical, chemical and biological phenomena involved in the process of contaminant removal in biofilters is developed. In addition to the contaminant, the key components of the present theoretical model are carbon dioxide and oxygen. The model predicts the concentration profile of the key components in the gas phase, the biofilm and the sorption liquid retained in the solid particles composing the filter bed at both steady and transient regimes. The model equations were solved numerically and comparison between theory and experiment showed that the model results for styrene and carbon dioxide concentration profiles were in very good agreement with experimental data for the biofiltration of styrene vapors at steady state. The analysis of oxygen concentration profile in the biofilm predicted by the theoretical model revealed that oxygen limitation does not occur under the operating styrene biodegradation rate in the biofilter. Copyright © 2003 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.258
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations36
Published2003
Admission routes2
Has abstractyes

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