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Record W2167543845 · doi:10.1080/03601234.2012.646175

Microwave enhanced advanced oxidation process for treating dairy manure at low pH

2012· article· en· W2167543845 on OpenAlexafffund
K.V. Lo, Winnie Weng I Chan, Selina Kuukuwa Yawson, Ping Liao

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2012
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaConsejería de Educación e Investigación
KeywordsManureProcess (computing)Oxidation processPulp and paper industryEnvironmental scienceWaste managementChemistryAgronomyComputer scienceEngineeringBiology

Abstract

fetched live from OpenAlex

This study investigated the treatment of dairy manure using the microwave enhanced advanced oxidation process (MW-AOP) at pH 2. An experimental design was developed based on a statistical program using response surface methodology to explore the effects of temperature, hydrogen peroxide dosage and heating time on sugar production, nutrient release and solids destruction. Temperature, hydrogen peroxide dosage and acid concentration were key factors affecting reducing sugar production. The highest reducing sugar yield of 7.4% was obtained at 160°C, 0 mL, 15 min heating time, and no H(2)O(2) addition. Temperature was a dominant factor for an increase of soluble chemical oxygen demand (SCOD) in the treated dairy manure. The important factors for volatile fatty acids (VFA) production were microwave temperature and hydrogen peroxide dosage. Temperature was the most important parameter, and heating time, to a lesser extent affecting orthophosphate release. Heating time, hydrogen peroxide dosage and temperature were significant factors for ammonia release. There was a maximum of 96% and 196% increase in orthophosphate and ammonia concentration, respectively at 160°C, 0.5 mL H(2)O(2) and 15 min heating time. The MW-AOP is an effective method in dairy manure treatment for sugar production, nutrient solubilisation, and solids disintegration.

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 score0.031
Threshold uncertainty score0.208

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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

Citations12
Published2012
Admission routes2
Has abstractyes

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