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Record W2579955885 · doi:10.2166/wqrjc.2017.004

Sludge-bed anaerobic membrane bioreactor treating prehydrolysis liquor (PHL): correlation of cATP™ with performance parameters

2015· article· en· W2579955885 on OpenAlexaff
Mayur Milan Kale, Kripa Shankar Singh, Dibyendu Debnath, Dennis A. Connor

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsVolatile suspended solidsBioreactorAnaerobic exerciseChemical oxygen demandChemistryPulp and paper industryBiodegradationBiomass (ecology)Correlation coefficientChromatographyWastewaterEnvironmental scienceEnvironmental engineeringBiologyComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Cellular adenosine triphosphate (cATP™) may be used to characterize the biological degradation activity within the anaerobic and aerobic wastewater treatment systems. The feasibility of using cATP™ as a process monitoring parameter for an anaerobic membrane bioreactor was evaluated. Results obtained from batch respirometric anaerobic treatability studies and a continuous sludge-bed anaerobic membrane bioreactor (SB-AnMBR) treating prehydrolysis liquor were examined. Both studies were conducted at an operating temperature of 35 °C. The batch studies indicated that the indirect activity measurements, such as the methane production rate correlated well with cATP™. The cATP™ content of the SB-AnMBR responded to changes in total amount of degraded chemical oxygen demand (COD) and methane produced with correlation coefficient of 0.91 and 0.80, respectively. In contrast, cATP™ was not well correlated to mixed liquor volatile suspended solids with correlation coefficient of 0.52. The values of cATP™/CODremoved ratio and active biomass ratio (ABR™) indicated that the increase in cATP™ value might be a result of increased biodegradation activity in the SB-AnMBR system.

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)0.006
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.349
Teacher spread0.241 · 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 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
Published2015
Admission routes1
Has abstractyes

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