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Record W2034248094 · doi:10.1504/ijetm.2011.039258

Effect of microwave temperature, intensity and moisture content on solubilisation of organic fraction of municipal solid waste

2011· article· en· W2034248094 on OpenAlexaff
Haleh Shahriari, Mostafa Warith, Kevin J. Kennedy

Bibliographic record

VenueInternational Journal of Environmental Technology and Management · 2011
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsChemistryChemical oxygen demandAnaerobic digestionFraction (chemistry)MoistureMunicipal solid wasteWater contentChromatographyWastewaterMethaneWaste managementEnvironmental scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

High temperature and pressure microwave (MW) pre-treatment of the Organic Fraction of Municipal Solid Waste (OFMSW) enhanced solubilisation prior to Anaerobic Digestion (AD). Three temperatures (175°C, 145°C and 115°C), three MW intensities based on temperature ramp times (20, 40 and 60 minutes) and two Supplemental Water Additions (SWA) of 20% and 30% were evaluated. MW irradiation resulted in higher concentrations of soluble Chemical Oxygen Demand (sCOD), proteins and sugars in the supernatant phase. The highest level of solubilisation was achieved at 175°C and SWA of 30% and resulted in 1.61, 1.62 and 1.58 times higher sCOD concentrations versus controls for MW intensity ramp times of 20, 40, and 60 minutes, respectively. Additionally, for the same conditions, the free liquid volume released from the OFMSW into the supernatant were observed to be 1.39, 1.34 and 1.37 times greater than the control, respectively. Concomitantly, potentially bio-available sCOD available for AD increased more than two fold compared to control.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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Same venueInternational Journal of Environmental Technology and ManagementSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207