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Record W7011605837

A Novel Technique for Hydrogen Production from Hog-Manure in Supercritical Partial Oxidation (SCWPO)

2010· article· de· W7011605837 on OpenAlexafffund

Bibliographic record

VenueJuSER (Forschungszentrum Jülich) · 2010
Typearticle
Languagede
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercritical water oxidationPartial pressureSupercritical fluidPartial oxidationChemical oxygen demandHydrogenAmmoniaOxygenBatch reactor
DOInot available

Abstract

fetched live from OpenAlex

In this study, the catalytic hydrogen production from hog manure using supercritical water partial oxidation was investigated in a batch reactor at a temperature of 500 C, and pressure of 28 MPa using several metallic catalysts.Hog manure was characterized by a total and soluble chemical oxygen demand (TCOD, SCOD) of 57000 and 28000 mg/L, total and volatile suspended solids (TSS, VSS) of 25000, 19000, and ammonia of 2400 mg/L, respectively.The order of H 2 production was the following: Pd/AC > Ru/Al 2 O 3 > Ru/AC > AC > NaOH.The order of COD reduction efficiency was as follows: NaOH > Ru/AC > AC > Ru/Al 2 O 3 > Pd/AC.The behaviour of the volatile fatty acids (VFA's), ethanol, methanol, ammonia, H 2 S, and Sulfate was investigated experimentally and discussed.A 35 % reduction in the H 2 and CH 4 yields was observed in the sequential gasification partial oxidation (oxidant at an 80 % of theoretical requirement) experiments compared to the gasification experiments (catalyst only).Moreover, this reduction in gas yields was coincided with a 45 % reduction in the liquid effluent chemical oxygen demand (COD), 60 % reduction of the ammonia concentration in the liquid effluent, and 20 % reduction in the H 2 S concentration in the effluent gas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.006

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 head0.224
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2010
Admission routes2
Has abstractyes

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