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Record W2159825504 · doi:10.4314/wsa.v33i2.49085

Microwave enhanced digestion of aerobic SBR sludge

2009· article· en· W2159825504 on OpenAlexafffund
K.J. Kennedy, Gabriel Thibault, RL Droste

Bibliographic record

VenueWater SA · 2009
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Ottawa
FundersBIOCAP Canada
KeywordsChemical oxygen demandMesophileAnaerobic digestionChemistryPulp and paper industryMethaneBiogasVolatile suspended solidsSewage sludgeWaste managementWastewaterSewage treatmentEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Factorial experiments were carried out to determine the potential of microwaves (MWs) for improving characteristics of aerobic sequencing batch reactor (SBR) sludge to enhance mesophilic anaerobic digestion. Effects of pretreatment temperature, MW irradiation intensity and solids concentration on sludge characterisation parameters were monitored. Increasing pretreatment temperature in the 45 to 85ºC range increased the soluble COD/total COD (chemical oxygen demand) ratio.MW intensity and sludge concentration in the 1 to 5% (w/v) had minimal effects on solubilisation of COD. Biochemical methane potential (BMP) tests at 35oC used to investigate effects of MW temperature, number of MW cycles and partial SBR sludge pretreatment showed that partial MW pretreatment of sludge and increased MW exposure cycles does not significantly improve overall methane production. In general, BMP tests demonstrated that 100% of SBR sludge irradiated once to 85ºCproduced the greatest improvement in VS destruction (12%) and overall methane production (16%). Generally improved biogas production via MW pretreatment was not accompanied by any potential improvement in sludge dewaterability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2009
Admission routes2
Has abstractyes

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