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Record W2006764317 · doi:10.1080/09593332608618612

Treatment of Wastewater Containing a Large Amount of Suspended Solids by a Novel Multi-Staged UASB Reactor

2005· article· en· W2006764317 on OpenAlexaff
Shigeki Uemura, Hideki Harada, Akiyoshi Ohashi, S. Torimura

Bibliographic record

VenueEnvironmental Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsSuspended solidsEffluentChemical oxygen demandWastewaterTotal suspended solidsVolatile suspended solidsChemistrySewage treatmentPulp and paper industryWaste managementSeparator (oil production)BiogasEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Treatment of artificial wastewater containing a large amount of suspended solids comprised of soybean processing waste and pig fodder was studied using a novel multi-staged upflow anaerobic sludge blanket reactor. The reactor consisted of three compartments, each containing a gas solid separator. The wastewater had chemical oxygen demand of approximately 21600 mg l(-1), suspended solids of 12800 mg l(-1), and an ammonia concentration of 945 mg l(-1). A continuous experiment without effluent circulation showed that the multi-staged reactor was not that effective for the treatment of wastewater containing a large amount of suspended solids. However, operation of the reactor with circulation of effluent enabled the reactor to achieve organic removal of 85% and approximately 70% methane conversion at loading rates of between 4.0 to 5.4 kg-chemical oxygen demand per cubic meter per day, meaning that the reactor was more effective when effluent was circulated. Morphological investigation revealed that the crude fiber in the sludge was partially degraded and that it had many small depressions on its surface. Evolved biogas may have become caught in these depressions of the fibers and caused washout of the sludge.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.064
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)0.0020.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

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