MétaCan
Menu
Back to cohort
Record W2604771897 · doi:10.1002/cjce.22856

The effect of packing on direct contact evaporation in spray column

2017· article· en· W2604771897 on OpenAlexvenueno aff
Runzhi Hu, Yanchao Jin, Qunwu Huang, Yiping Wang, Yong Cui

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsRaschig ringPressure dropMaterials scienceBoiling pointCountercurrent exchangeEvaporationHeat transferHeat transfer coefficientPacked bedInletPhase (matter)Volumetric flow rateBoilingAnalytical Chemistry (journal)Composite materialThermodynamicsChromatographyChemistry

Abstract

fetched live from OpenAlex

Abstract The direct contact evaporation in countercurrent spray column has been experimentally investigated. Two kinds of random packing were added into the column independently to study their influence on heat transfer process. Three different continuous phase flow rates (150, 200, 250 L/h) and two different dispersed flow rates (6.565, 13.13 L/h) were used in the experiment. The inlet continuous phase temperature ranged from 42 to 57 °C, and the inlet dispersed phase temperature was near its boiling point. The effect of variable factors on the optimal column height, the volumetric heat transfer coefficient, the pressure drop, and the thickness of foam layer were studied. The results showed that packing had positive effects on heat transfer, such as decreasing the optimal column height and the thickness of foam layer, increasing heat transfer coefficient. But extra pressure was also obvious. Dixon rings performed better than Raschig rings in the experiments due to their finer structure.

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)0.002
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and Heat TransferFrench-language works237,207