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Record W2166261363 · doi:10.2166/wqrjc.2011.122

Anaerobic co-digestion of municipal wastewater sludge and restaurant grease

2011· article· en· W2166261363 on OpenAlexafffund
Zengkai Liu, Ian Buchanan

Bibliographic record

VenueWater Quality Research Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnaerobic digestionBiogasAlkalinityChemical oxygen demandMesophilePulp and paper industryWastewaterGreaseMethaneChemistryWaste managementDigestion (alchemy)Environmental scienceEnvironmental engineeringChromatographyEngineering

Abstract

fetched live from OpenAlex

Co-digestion has drawn much attention because of its potential to increase biogas production among other benefits. Restaurant grease has high energy content and methane production potential and so is a promising substrate for co-digestion with municipal wastewater sludge. Anaerobic co-digestion of municipal wastewater sludge and restaurant grease was investigated at laboratory-scale under mesophilic conditions (37 °C). Increasing the chemical oxygen demand (COD) loading rate of the test digester by 387% (organic loading rate 4.235 kg VS/m3/d) relative to the control digester led to a 467% increase in daily biogas production, a 25.2% increase in methane yield (based on volatile solids (VS) destruction), a 29.8% increase in COD removal and a 27.2% increase in VS reduction. The biogas methane content ranged from 62.6 to 66.2% (v/v). No negative effect of the grease addition was observed in the test digester performance under the conditions investigated. The increases in biogas production and methane yield indicated enhanced digestion performance. In addition, partial alkalinity and pH proved to be good indicators of digester stability.

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)0.004
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 score0.043
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
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 head0.166
GPT teacher head0.358
Teacher spread0.192 · 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

Citations8
Published2011
Admission routes2
Has abstractyes

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