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Record W4362700600 · doi:10.1002/aic.18100

Phenol biodegradation in a bioreactor considering different geometries and process parameters

2023· article· en· W4362700600 on OpenAlexafffund
Mojtaba Aghajani Delavar, Junye Wang

Bibliographic record

VenueAIChE Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsAthabasca University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioreactorPhenolInletBiodegradationChemistryLattice Boltzmann methodsWastewaterPulp and paper industryChromatographyEnvironmental engineeringChemical engineeringEnvironmental scienceMechanicsOrganic chemistryGeologyPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract In this study, a three‐dimensional coupled lattice Boltzmann model and cellular automata platform was developed to simulate biofilm growth and phenol biodegradation as an effective and sustainable way to remove phenolic contaminants in aquatic systems. Two three‐dimensional bioreactors with cubic or spherical obstacles at varying inlet phenol concentrations and flow velocities were examined. The results showed that the cubic‐bioreactor had higher phenol reduction than the spheric‐bioreactor due to the greater effects of cubic obstacles on flow patterns. The biofilm concentration in bioreactors decreased up to 36.4% as inlet velocity increased and in the spheric‐bioreactor at the same inlet phenol concentration. The cubic‐bioreactor had the highest normalized reduction rate of 1.194 at the lowest inlet phenol concentration, while the spheric‐bioreactor had the lowest one of 0.871 at the highest inlet phenol concentration. The results proved the accuracy of the model to assess the performance of wastewater treatment bioreactors under different conditions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.363

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 head0.034
GPT teacher head0.268
Teacher spread0.233 · 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 designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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