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Development of The Oxygen Mass Balance Equation for aerobic bioreactors

2020· preprint· en· W3106882829 on OpenAlexaff
Johnny Lee

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsConestoga College
Fundersnot available
KeywordsAerationMass transfer coefficientMass transferOxygenProcess (computing)BioreactorChemistryProcess engineeringWastewaterEnvironmental scienceBiochemical engineeringBiological systemEnvironmental engineeringComputer scienceChromatographyEngineering

Abstract

fetched live from OpenAlex

This paper addresses a problem common to all the standards, which is the application of clean water test result to Process Oxygen Transfer Rates. By modifying the conventional model used by the standards for this application, and if proper OUR (oxygen uptake rate) methods can accurately determine the respiration rate, this paper attempts to show that the clean water tests can be used to determine the oxygen transfer efficiency of an aeration device in the field. The new model is based on previously developed mathematical models, and also based on the novel concept of a resistance to gas transfer due to microbial activity in the field. It is postulated that the relative mass transfer coefficient, α (alpha), the ratio of mass transfer coefficient in wastewater KLaf to mass transfer coefficient in water KLa, is independent of microbial activities. The field-determined OTEpw is mathematically associative to the transfer process by addition.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.054
GPT teacher head0.260
Teacher spread0.206 · 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
GenreMethods

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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