MétaCan
Menu
Back to cohort
Record W2067166310 · doi:10.1080/09593330.2004.9619352

Municipal Wastewater Treatment by USAB Process: Start-up at 20°c and Operation at Low Temperatures

2004· article· en· W2067166310 on OpenAlexafffund
K. S. Singh, T. Viraraghavan

Bibliographic record

VenueEnvironmental Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWastewaterProcess (computing)Waste managementEnvironmental scienceSewage treatmentEnvironmental engineeringProcess engineeringPulp and paper industryChemistryEngineeringComputer science

Abstract

fetched live from OpenAlex

Laboratory-scale UASB reactors were started-up successfully at 20 degrees C and operated at temperatures of 32, 20, 15, 11, and 6 degrees C applying several hydraulic retention times (HRTs) ranging from 48 to 3 h during an operational period of approximately 900 days. Changes in temperature and HRTs impacted the reactor performance. However, overall reactor performance (70 to 90% COD removal) was found to be stable up to an HRT of 6 h and temperature of 11 degrees C. The performance of UASB reactor was not very stable during 6 degrees C operation, even though 30 to 50% of COD removal could be achieved. Biomass aggregation in the form of granules/bio-pellets (mean size ranged from 1.8 mm to 3.0 mm) could be achieved during 20 degrees C operation. The impact of temperature on morphology, surface structure, and shape of bio-pellets was explored. Morphological and elemental composition analyses showed the possible mechanism of biomass aggregation in UASB reactors. This study demonstrated that the UASB process could be applied successfully with some minor adjustment for the treatment of municipal wastewater in temperate and cold regions (average summer temperature 11-25 degrees C).

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 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 score0.016
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations8
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental TechnologySame topicMembrane Separation TechnologiesFrench-language works237,207