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Record W2042460023 · doi:10.1080/03601234.2010.515474

Treatment of dairy manure using the microwave enhanced advanced oxidation process under a continuous mode operation

2010· article· en· W2042460023 on OpenAlexaff
Yang Yu, Ing W. Lo, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsManureMicrowaveMode (computer interface)Process (computing)Environmental scienceProcess engineeringOxidation processAgricultural engineeringMaterials scienceAgronomyPulp and paper industryComputer scienceEngineeringTelecommunicationsBiology

Abstract

fetched live from OpenAlex

The microwave enhanced advanced oxidation process (MW/H(2)O(2)-AOP) was used to treat dairy manure for solubilization of nutrients and organic matters. This study investigated the effectiveness of the MW/H(2)O(2)-AOP under a continuous mode of operation, and compared the results to those of batch operations. The main factors affecting solubilization by the MW/H(2)O(2)-AOP were heating temperature and hydrogen peroxide dosage. Soluble chemical oxygen demand (SCOD) and volatile fatty acids (VFA) increased with an increase of microwave (MW) heating temperature; very high concentrations were obtained at 90°C. Insignificant amounts of ammonia and reducing sugars were released in all runs. An acidic pH condition was required for phosphorus solubilisation from dairy manure. The best yield was obtained at 90°C with an acid dosage of 1.0 %; about 92 % of total phosphorus and 90 % of total chemical oxygen demand were in the soluble forms. The MW/H(2)O(2)-AOP operated in a continuous operation mode showed pronounced synergistic effects between hydrogen peroxide and microwave irradiation when compared to a batch system under similar operating conditions, resulting in much better yields.

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)0.000
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 score0.047
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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