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Record W4386604619 · doi:10.1680/jenes.22.00073

Anaerobic digestion of thickened secondary sludge after microwave advanced oxidation pretreatment

2023· article· en· W4386604619 on OpenAlexaffvenue
Sarah Ries, Ping Liao, Donald S. Mavinic, Victor S. Y. Lo

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemical oxygen demandAnaerobic digestionPulp and paper industryHydraulic retention timeAnaerobic exerciseMethaneChemistryBioreactorSubstrate (aquarium)OxygenWaste managementMicrowaveChemical engineeringWastewaterOrganic chemistryBiology

Abstract

fetched live from OpenAlex

A microwave-enhanced advanced oxidation process (MW-AOP) was used to treat thickened waste secondary sludge prior to anaerobic digestion (AD). A large amount of soluble chemical oxygen demand (SCOD) and volatile fatty acids (VFAs) was formed in the solution after pretreatment. Both single- and two-phase systems loaded up to 18.2 g volatile solids/(l day) while achieving high methane production, as well as high overall treatment efficiency. Both the upflow anaerobic blanket reactor and the continuous stirred tank reactor could operate at very short hydraulic retention times as short as 2 days without any evidence of souring. The results indicated that an acid reactor in the two-phase system was not required, due to very high levels of SCOD and VFAs produced in the MW-AOP-pretreated solution. As such, a single AD should be able to handle this easily biodegradable substrate following microwave pretreatment.

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 score0.001
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 integrity0.0000.001
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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