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Record W2076210218 · doi:10.1002/aic.14749

A novel induction heating fluidized bed reactor: Its design and applications in high temperature screening tests with solid feedstocks and prediction of defluidization state

2015· article· en· W2076210218 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFluidized bedDistributorNuclear engineeringMaterials scienceRaw materialLift (data mining)Waste managementInduction heatingTube (container)FluidizationProcess engineeringEnvironmental scienceMechanical engineeringEngineeringChemistryComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

A novel mini induction heating fluidized bed reactor (IHFBR) is introduced which was developed to carry out screening tests of high temperature reactions up to 1500°C particularly for solid feedstocks. Despite conventional mini reactors, this reactor mimics real scenario of solid feeding in industrial reactors: cold feedstock is injected within 1 s from a lift tube, then particles reach reaction temperature in less than 5 s in a reaction zone. The lift tube (9.5 cm diameter) is also the gas distributor of the fluidized bed (2.5 cm diameter) so that the bed is completely fluidized with uniform gas distribution. Beside facilities to perform tests in a fluidized bed, another important feature of this reactor is prediction of the defluidization state in the bed. Not only reproducible data are generated, but also many tests can be conveniently carried out, that is, one test per hour. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1507–1523, 2015

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.

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 score0.117
Threshold uncertainty score0.363

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 head0.028
GPT teacher head0.240
Teacher spread0.213 · 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