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Record W3211036917 · doi:10.32920/ryerson.14653770.v1

Characterization of a hybrid sequencing batch reactor system for treatment of wastewater using suspended microbial aggregates and biofilms

2021· preprint· en· W3211036917 on OpenAlexaff
Romeo Gabriel Dumitrache

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNitrificationWastewaterChemical oxygen demandMoving bed biofilm reactorBiofilmBiomass (ecology)Suspended solidsSurface-area-to-volume ratioPulp and paper industryHybrid reactorVolatile suspended solidsHydraulic retention timeAmmoniaChemistryEnvironmental scienceEnvironmental engineeringChemical engineeringMaterials scienceNitrogenEcologyBacteria

Abstract

fetched live from OpenAlex

A moving bed biofilm reactor was studied for its capability of carbon oxidation and nitrification. The hybrid system made use of suspended biomass in the forms of microbial aggregates and attached biomass in the form of biofilms on suspended carriers. The carriers used for biofilm support were made of polyethylene and have a wagon wheel shape. The carrier fill ratio, which is defined as the volume ratio of carrier to the whole reactor was a key characteristic parameter of the reactor. The experimental runs used different carrier filling ratios from 25 to 50% to determine the optimal operating value for this type of hybrid reactor. Also the nutrient conditions were modified to test the capacity of the system to adapt to various changes in phosphorus loading in the influent wastewater. The results showed that for an influent chemical oxygen demand (COD) of 600 mg/L, ammonia of about 24 mg/L and hydraulic reaction time of 6 hours there was no difference in the performance of the system under the different carrier filling rations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.028
GPT teacher head0.230
Teacher spread0.202 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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