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Record W2059933842 · doi:10.1080/03601230009373277

Swine wastewater treatment in a two stage sequencing batch reactor using real‐time control

2000· article· en· W2059933842 on OpenAlexafffund
Norman Cheng, K.V. Lo, K.H.S. Yip

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerationSequencing batch reactorHydraulic retention timeAnoxic watersWastewaterPulp and paper industryBatch reactorRetention timeChemistrySewage treatmentEnvironmental scienceAmmoniaWaste managementEnvironmental engineeringChromatographyEnvironmental chemistryEngineering

Abstract

fetched live from OpenAlex

A laboratory scale two-stage sequencing batch reactor (TSSBR) was used to study the effectiveness of pH as a real-time control parameter in swine wastewater treatment. A Ringlace media was inserted into the A/O (Anoxic/Oxic) reactor for bacteria immobilization. The TSSBR was subjected to three levels of organic loading. The pH and ORP (Oxidation Reduction Potential) patterns obtained were consistent with distinct features, enabling the real-time control strategy to effectively set a flexible aeration time pending on influent concentration, hence resulting in flexible cycle time and HRT (Hydraulic Retention Time) for the system. The real-time process ensured a removal efficiency of over 99% and 95%, respectively, for ammonia and TOC (Total Organic Carbon). For NO3(-)-N and PO4(-3), the run with influent TOC = 4,000 mg/L yielded the most efficient removal of 61% and 95%, respectively. Test results suggest that pH can be a viable tool for on-line real-time control of a biological treatment process.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.292
Teacher spread0.261 · 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.

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

Citations17
Published2000
Admission routes2
Has abstractyes

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