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Record W2982198335 · doi:10.32964/tj13.8.19

A laboratory study of recovery boiler smelt shattering

2014· article· en· W2982198335 on OpenAlexfundno aff
Anton Taranenko, Markus Bussmann, Honghi Tran

Bibliographic record

VenueTAPPI Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNozzleBoiler (water heating)SmeltMechanicsViscosityVolumetric flow rateJet (fluid)Air streamEnvironmental scienceChemistryMaterials scienceWaste managementEngineeringComposite materialMechanical engineeringEnvironmental engineeringPhysicsFishery

Abstract

fetched live from OpenAlex

A scaled-down experimental apparatus was built to examine smelt shattering during typical recovery boiler operations. Water-glycerine solutions and air were used in place of smelt and steam. A high-speed camera and image processing software were used to record and quantify liquid shattering in terms of droplet number and size distributions, as a function of air velocity, air nozzle position, liquid flow rate, and liquid viscosity. The results showed that increasing shatter jet velocity reduced average droplet size, increasing the liquid flow rate increased droplet size, and placing the shatter jet nozzle closer to the liquid stream decreased droplet size. These results were all as expected. The effect of liquid viscosity (1-50 cP) depended on the shatter jet velocity. At high air velocities, even the viscous liquid was well shattered, but at lower velocities, the effect of viscosity on shattering was significant.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.181
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 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
Published2014
Admission routes1
Has abstractyes

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