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Record W2467822886 · doi:10.2175/106143000x137130

Application of Feedback Control Based on Dissolved Oxygen to a Fixed‐Film Sequencing Batch Reactor for Treatment of Brewery Wastewater

2000· article· en· W2467822886 on OpenAlexfundno aff
Anh‐Long Nguyen, Sheldon J.B. Duff, John D. Sheppard

Bibliographic record

VenueWater Environment Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsClarifierSequencing batch reactorEffluentWastewaterChemical oxygen demandSuspended solidsSewage treatmentBiochemical oxygen demandBatch reactorTotal suspended solidsPulp and paper industryBioreactorMixed liquor suspended solidsWaste managementVolatile suspended solidsChemistryOxygenEnvironmental scienceActivated sludgeEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Treatment of brewery wastewater was performed using an aerobic fixed‐film sequencing batch reactor using a feedback control system based on dissolved oxygen concentration in the mixed liquor. This control system was modeled after the self‐cycling fermentation technique. The system was operated at 25 and 35 °C and under growthrate limiting conditions. Total biochemical oxygen demand (TBOD) removal efficiency was 83% after 3 hours at 25 °C and 92% after 1.5 to 2 hours at 35 °C. The treated effluent produced had a TBOD between 120 and 438 mg/L, with suspended solids contributing between 63 and 71% of the TBOD. Effectiveness of the treatment process might be improved if an efficient secondary clarifier was installed or if the suspended solids were removed from the brewery wastewater before treatment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0040.001

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 head0.029
GPT teacher head0.270
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; both teacher heads agree on what is shown here.

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

Citations16
Published2000
Admission routes1
Has abstractyes

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