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Record W2018516201 · doi:10.1080/10934520701418706

An ozone/hydrogen peroxide/microwave-enhanced advanced oxidation process for sewage sludge treatment

2007· article· en· W2018516201 on OpenAlexafffund
Guiqing Yin, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrogen peroxideChemistrySewage sludgeOzoneSewage treatmentAdvanced oxidation processMicrowaveEnvironmental chemistryWaste managementCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Solids destruction and nutrients release from sewage sludge were investigated using thermal destruction and/or oxidation processes. Hydrogen peroxide (H(2)O(2)), ozone (O(3)) and a combination of both were used for the oxidation processes performed at ambient temperature. Thermal destruction using microwave (MW) alone without an oxidant was also conducted. Microwave enhanced advanced oxidation processes (MW-AOP), such as MW/O(3), MW/H(2)O(2) and MW/H(2)O(2)/O(3), were conducted at 100 degrees C. In terms of nutrients release and solids reduction, the MW/H(2)O(2)/O(3)-AOP yielded the best result; an addition of ozone improved the MW-AOP process. More than 30% of TP and 20% of TKN were released into the solution. About 37% of total COD was also solubilized from sludge mass. Both the conventional oxidation processes and the MW-AOP processes could be used to release nutrients and to reduce solids from sewage sludge; however, the MW-AOP processes were superior in performance. Microwave heating alone also resulted in a substantial amount of ortho-phosphate into the solution.

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 score0.001

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)0.0000.000

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 head0.025
GPT teacher head0.318
Teacher spread0.292 · 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

Citations49
Published2007
Admission routes2
Has abstractyes

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Same venueJournal of Environmental Science and Health Part ASame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207