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Record W2116151768 · doi:10.4137/aswr.s27218

Polyhydroxybutyrate Production from Municipal Wastewater Activated Sludge with Different Carbon Sources

2015· article· en· W2116151768 on OpenAlexaff
Qiuyan Yuan, Richard Sparling, Jan A. Oleszkiewicz

Bibliographic record

VenueAir Soil and Water Research · 2015
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPolyhydroxybutyrateWastewaterActivated sludgePhosphorusEnhanced biological phosphorus removalPolyphosphatePulp and paper industryPolyhydroxyalkanoatesSequencing batch reactorWaste managementChemistrySewage treatmentActivated carbonSubstrate (aquarium)Carbon fibersFood scienceBiologyBiochemistryBacteriaMaterials scienceAdsorptionPhosphateOrganic chemistryEcology

Abstract

fetched live from OpenAlex

In this study, a sequencing batch reactor was set up and operated for over three months to cultivate polyphosphate-accumulating organisms polyphosphate-accumulating organisms (PAOs) in the enriched activated sludge. Batch studies were then carried out to study the effect of different carbon sources on phosphorus removal as well as polyhydroxybutyrate (PHB) production. The carbon sources investigated were acetate, glucose, wastewater, and beef extract. It was found that enhanced biological phosphorus removal could not be achieved using glucose as substrate. This suggested that glucose was not a good candidate for biological phosphorus removal. In terms of PHB production, using acetate and glucose as substrate resulted in PHB production of 42% and 40%, respectively, of the dry cell weight (DCW). Lower PHB production was obtained from using municipal wastewater and beef extract as a carbon source. This resulted in ∼15% and 13% of DCW. It was concluded that municipal wastewater activated sludge can be an economic alternative for PHB production if municipal wastewater is mixed with certain kinds of carbon-enriched industrial wastewater.

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 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 score0.019
Threshold uncertainty score0.987

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.273
Teacher spread0.199 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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