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Record W2065530620 · doi:10.1081/pfc-100103743

SWINE WASTEWATER TREATMENT USING ATTACHED-GROWTH AND SUSPENDED-GROWTH TWO STAGE SEQUENCING BATCH REACTORS WITH REAL-TIME CONTROL

2001· article· en· W2065530620 on OpenAlexaff
Norman Cheng, K.V. Lo, K.H.S. Yip

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAerationEffluentSequencing batch reactorWastewaterPulp and paper industryChemistryTotal organic carbonSewage treatmentBioreactorCarbon sourceEnvironmental scienceEnvironmental engineeringEnvironmental chemistryBiochemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Two two-stage sequencing batch reactors (TSSBR), one attached-growth and one suspended-growth, were operated under three levels of wastewater concentration (approximately 4,000, 2,000 and 500 TOC mg/L), respectively, to compare the pH and ORP (oxidation-reduction potential) patterns and system performance. In both TSSBR systems, the pH and ORP profiles varied with organic loading yet exhibited consistent patterns with distinctive features suitable for real-time control. For all runs at the three levels of influent, both systems achieved similar levels of treatment for BOD5, TOC and TSS of over 97.5, 93.4, and 97.3%, respectively. The attached-growth system out performed the suspended-growth system in achieving the same levels of treatment at much shorter aeration cycle times. The treatment efficiency for NO3(-)-N and PO4(-3) was greatly affected by the carbon content in the wastewater, and the best treatment was achieved during the TOC approximately 4,000 mg/L runs with final effluent at 4.0 and 21.3 mg/L, respectively.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.816

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.278
Teacher spread0.247 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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Same venueJournal of Environmental Science and Health Part BSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207