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Record W2054238475 · doi:10.1002/cjce.22118

Anaerobic co‐digestion of dairy cattle slurry and agro‐industrial fats: Effect of fat ratio on the digester efficiency

2014· article· en· W2054238475 on OpenAlexvenueno aff
Sébastien Guillaume, Thomas Lendormi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
Fundersnot available
KeywordsSlurryDigestion (alchemy)ChemistryAnimal scienceOrganic matterDry matterAnaerobic digestionFood scienceBiodegradationProductivityMethanePulp and paper industryEnvironmental scienceBiologyChromatographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The objective of the study was to optimize the co‐digestion of cattle slurry in the presence of fats from the food industry. A laboratory pilot plant operating continuously was used for the experimental study. Tests were carried out with only cattle slurry and with different fat ratio (0, 10, 25, 45 and 60 % w/w of the feed COD as fats). In the case of using cattle slurry feed and OLR from 0.4 and 3.0 kg VS · m −3 · day −1 (HRT between 125 days and 19 days), methane productivity versus OLR exhibit a linear relation reflecting optimum biodegradability of the organic matter. For OLR above 3 kg VS · m −3 · day −1 (HRT less than 19 days), a break in the slope is observed expressing a decrease of the organic matter degradation rate. For HRT equal to 30 days, the results show that fat incorporation ratio lower than 25 % w/w does not affect the digester operation while maintaining acceptable biodegradability of the fat. In contrast, with a fat ratio of 60 % w/w , the system became unstable and a significant decrease in the methane productivity was observed, due to an accumulation of undegraded Long Chain Fatty Acids (LCFA). The conclusion is that the fat ratio must be controlled to avoid the destabilization of digestion, and finally operational conditions are proposed for co‐digestion.

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 score0.359
Threshold uncertainty score0.313

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207