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Record W2472369295 · doi:10.2175/106143006x99812

Treatment of High‐Strength Pet Food Wastewater Using Two‐Stage Membrane Bioreactors

2006· article· en· W2472369295 on OpenAlexaff
C. Acharya, George Nakhla, A. Bassi, R. Kurian

Bibliographic record

VenueWater Environment Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsBioreactorWastewaterMembrane bioreactorPet foodStage (stratigraphy)Sewage treatmentWaste managementEnvironmental sciencePulp and paper industryChemistryEnvironmental engineeringFood scienceBiologyEngineeringBotany

Abstract

fetched live from OpenAlex

A two-stage membrane bioreactor was used to treat dissolved-air-flotation pretreated, high-strength pet food wastewater characterized by oil and grease concentrations of 50 000 to 82 000 mg/L and total chemical oxygen demand (COD) and five-day biochemical oxygen demand (BOD5) concentrations of 100 000 and 80 000 mg/L, respectively, to meet stringent surface discharge criteria (i.e., BOD5, total suspended solids [TSS], and ammonium-nitrogen [NH4(+)-N] of < 10 mg/L at an overall hydraulic retention time of 6.3 days). Organic contaminants were removed primarily in the first stage, followed by almost complete removal of ammonia in the second stage. Despite a rise in poorly biodegradable COD in the second stage, overall removal of TSS, BOD5, COD, and ammonia was 100, 99.9, 95.2, and 99.7%, respectively, thus readily achieving the required criteria. Consistent nitrite accumulation over a period of more than 100 days, even at dissolved oxygen concentrations of more than 2.5 mg/L, was remarkable. A residual alkalinity requirement for nitrification was quantified. Membrane performance was extensively studied in this work.

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 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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.297
Teacher spread0.249 · 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
Published2006
Admission routes1
Has abstractyes

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