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Estimation of the Unbiodegradable Fraction of Thickened Waste Activated Sludge

2018· article· en· W2891175944 on OpenAlexaff
Mohammad Monirul Islam Chowdhury, George Nakhla, Jesse Zhu

Bibliographic record

VenueWater Environment Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsWestern University
Fundersnot available
KeywordsChemical oxygen demandEffluentHydraulic retention timeBiomass (ecology)Volatile suspended solidsActivated sludgeChemistryPulp and paper industryFraction (chemistry)Mixed liquor suspended solidsBiodegradationSuspended solidsAnaerobic digestionTotal suspended solidsAnaerobic exerciseEnvironmental scienceWastewaterEnvironmental engineeringChromatographyMethaneEcology

Abstract

fetched live from OpenAlex

The study investigated the unbiodegradable fraction of particulate chemical oxygen demand (PCOD) in thickened waste activated sludge (TWAS) using semicontinuous-flow completely-mixed anaerobic digesters. A laboratory-scale semicontinuous stirred tank reactor was used to investigate TWAS anaerobic biodegradability at hydraulic retention time of 16.7 d to 33.3 d and organic loading rate (OLR) of 1.21 kg COD/m3•d to 3.47 kg COD/m3•d. The COD and volatile suspended solids (VSS) removal for TWAS were 37% to 44% and 39% to 42% at an OLR of 1.27 and 3.47 kg COD/m3•d, respectively. Using a biomass yield (<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="00819-ilm01.gif"/>) of 0.29 g COD biomass/g COD substrate, decay rate (<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="00819-ilm02.gif"/>of 0.015 d-1; and a solids retention time (SRT) of 16.7 d; two linear fits correlating the difference between effluent biomass; and effluent particulate COD with influent total COD, the unbiodegradable fractions of PCOD and VSS were estimated from the slopes of the linear fits to be in the range of 0.26 to 0.28.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.279
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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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