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Record W2502258893 · doi:10.1080/03601234.2016.1208464

Microwave treatment of dairy manure for resource recovery: Reaction kinetics and energy analysis

2016· article· en· W2502258893 on OpenAlexafffund
Asha Srinivasan, Ping Liao, K.V. Lo

Bibliographic record

VenueJournal of Environmental Science and Health Part B · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of British Columbia
FundersMitacsGovernment of Canada
KeywordsManureChemical oxygen demandChemistryPhosphorusKineticsPulp and paper industryEnvironmental scienceSewage treatmentWastewaterWaste managementEnvironmental engineeringOrganic chemistryAgronomyPhysicsEngineering

Abstract

fetched live from OpenAlex

A newly designed continuous-flow 915 MHz microwave wastewater treatment system was used to demonstrate the effectiveness of the microwave enhanced advanced oxidation process (MW/H2O2-AOP) for treating dairy manure. After the treatment, about 84% of total phosphorus and 45% of total chemical oxygen demand were solubilized with the highest H2O2 dosage (0.4% H2O2 per %TS). The reaction kinetics of soluble chemical oxygen demand revealed activation energy to be in the range of 5–22 kJ mole−1. The energy required by the processes was approximately 0.16 kWh per liter of dairy manure heated. A higher H2O2 dosage used in the system had a better process performance in terms of solids solubilization, reaction kinetics, and energy consumption. Cost-benefit analysis for a farm-scale MW/H2O2-AOP treatment system was also presented. The results obtained from this study would provide the basic knowledge for designing an effective farm-scale dairy manure treatment system.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.256

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 communication0.0000.000
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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 designOther design
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

Citations9
Published2016
Admission routes2
Has abstractyes

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