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Record W2054012824 · doi:10.1080/10934520902719944

Solubilization of municipal anaerobic sludge using microwave-enhanced advanced oxidation process

2009· article· en· W2054012824 on OpenAlexafffund
Anju A. Kenge, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAmmoniaChemical oxygen demandHydrogen peroxideOxygenTotal dissolved solidsVolatile suspended solidsAnaerobic exerciseInorganic chemistryNuclear chemistryEnvironmental chemistryWaste managementWastewaterOrganic chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

The effect of microwave enhanced advanced oxidation process on the release of nutrients and solids disintegration from municipal anaerobic sludge was investigated. Three levels of temperature (160, 120, and 80 degrees C) and two sets of pH, (7.8 and 4) were selected and tested at a microwave heating time of 5 minutes and hydrogen peroxide dosage of approximately 1 mL per 1% total solids. Total chemical oxygen demand concentrations in samples decreased after the treatments at pH 4, while they remained the same as the initial at pH 7.8. The soluble chemical oxygen demand concentrations increased in all sets, regardless of treatment conditions. There was an increase in orthophosphate concentration with an increase of temperature at pH 4, while a decrease of orthophosphate at pH 7.8 was observed. Ammonia concentration also increased with an increase of temperature at pH 4. A small amount of ammonia was converted to nitrates/nitrites in the absence of acid. Acid addition helped in the destruction of sludge solids. Low pH and high temperature gave the highest nutrient release and solids destruction.

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 score0.364
Threshold uncertainty score0.436

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 communication0.0000.001
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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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

Citations13
Published2009
Admission routes2
Has abstractyes

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