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Record W2461018436 · doi:10.1002/cjce.22587

A study of the heat transfer characteristics of novel Ni‐foam structured catalysts

2016· article· en· W2461018436 on OpenAlexvenueno aff
Zheng Wan, Jian Jiang, Hongfang Ma, Yakun Li, Yong Lu, Fahai Cao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
Fundersnot available
KeywordsExothermic reactionHeat transferMaterials scienceHeat transfer coefficientPacked bedMetal foamCatalysisPorosityThermodynamicsEndothermic processHeat transfer enhancementChemical engineeringComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Highly exothermic reactions require catalyst beds with good heat transfer characteristics. Novel Ni‐foam structured catalysts have significant heat transfer efficiency compared with traditional packed beds, which is very suitable for the highly exothermic methanation reaction. The present work reports an experimental study of steady‐state heat transfer behaviour of a gas flowing through a fixed bed packed with Ni‐foam structured catalyst under various operation conditions. Experiments were carried out in a dedicated tubular reactor with inlet air temperature ranging from 160 to 200 °C, heating pipe temperature inside the fixed bed ranging from 400 to 500 °C, and air flow rate ranging from 4.0 to 6.0 Nm 3 /h. Effective radial conductivity ( λ er ) and wall heat transfer coefficient ( α w ) were derived based on the steady‐state measurements and the two‐dimensional pseudo‐homogeneous heat transfer model. The estimated values of λ er and α w were between 2.17 and 3.34 W/m · K and 170 to 250 W/m 2 · K respectively. Heat transfer properties of Ni‐foam structured catalyst are prominent compared with conventional packed bed. From the results it was found that the metal matrix and porous structure of the catalyst enhanced heat transfer. We anticipate that our research will open a new opportunity for design of new‐generation SNG process.

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)0.000
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 score0.008
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.010
GPT teacher head0.183
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 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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicHeat and Mass Transfer in Porous MediaFrench-language works237,207