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Record W2510784476 · doi:10.1002/cjce.22653

Continuous hydrogen production by dark fermentation in a foam SiC ceramic packed up‐flow anaerobic sludge blanket reactor

2016· article· en· W2510784476 on OpenAlexvenueno aff
Hong Sui, Jiao Dong, Mengjia Wu, Xingang Li, Ruiling Zhang, Guozhong Wu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBiohydrogenBlanketHydrogen productionHydraulic retention timeMaterials sciencePulp and paper industryWaste managementFermentationHydrogenDark fermentationPacked bedChemistryEffluentChromatographyComposite materialFood science

Abstract

fetched live from OpenAlex

Abstract Biohydrogen generation using anaerobic sludge from a sewage treatment plant is a promising strategy for converting waste into renewable energy. The porous foam SiC ceramic structured packing was used as a support in the up‐flow anaerobic sludge blanket (UASB) reactor to enhance the biohydrogen yield. Using glucose as the major carbon source, the SiC‐UASB reactor was continuously operated under the organic loading rates (OLR) between 30 and 90 gglucose/L · d at a hydraulic retention time (HRT) of 4 h. The maximum hydrogen production rate (5.24 L/L · d) and hydrogen content (50 %) were obtained at 60 gglucose/L · d OLR. It demonstrated that the reaction of fermentation was predominated by the butyric acid pathway. The employment of SiC packings resulted in quick startup (∼5 days) and high efficiency of hydrogen production. The buffering capacity and the cell culture immobilization ability of the UASB reactor were obviously improved by the application of SiC packings, which increased the feasibility of scale‐up of the continuous biohydrogen production 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.006
GPT teacher head0.179
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2016
Admission routes1
Has abstractyes

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