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Record W7070644353

Quantification of the Magnitude of Interparticle Forces in a Gas-Solid Fluidized Bed

2023· other· fr· W7070644353 on OpenAlexfundno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2023
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
FundersMitacs
KeywordsLimitingWork (physics)Filter (signal processing)Point (geometry)
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Les réacteurs à lit fluidisé de type gaz-solide sont largement utilisés dans de nombreuses industries chimiques en raison de leurs excellentes performances en termes de transfert de masse et de chaleur, dépassant ainsi celles d'autres contacteurs gaz-solide. Ces performances sont étroitement liées à l'hydrodynamique du lit, elle-même déterminée par l'intensité des forces inter particules (IPFs) et des forces hydrodynamiques (HDFs). Ces forces exercent une influence sur la conception, la mise à l'échelle, le fonctionnement et les performances globales du lit. Les IPFs exercent un impact significatif sur les caractéristiques hydrodynamiques du lit dans diverses situations, particulièrement dans les réacteurs à lit fluidisé catalytique. Ces situations comprennent notamment le groupe A de la classification de Geldart, où les IPFS sont du même ordre de grandeur que les HDFs. De même, elles inclurent le groupe C de la classification de Geldart, où les IPFs contrôlent le comportement hydrodynamique, et le fonctionnement à haute température, où l'ampleur des IPFs peut être beaucoup plus importante que dans les conditions ambiantes. Les IPFs et leurs effets sur l'hydrodynamique du réacteur à lit fluidisé ne sont pas toujours bien étudiés, en raison du manque de techniques de mesure permettant une quantification adéquate de ces forces dans le cas d'un lit fluidisé de type gaz-solide. En conséquence, cette étude se concentre principalement sur le développement d'approches fiables visant à quantifier avec précision l'ampleur des IPFs dans un lit fluidisé type gaz-solide. Par la suite, une intégration de l'effet des IPFs dans la classification de Geldart est envisagée afin de caractériser de manière rigoureuse la frontière entre les groupes A et C de la classification de Geldart. ABSTRACT: Many chemical industries take advantage of gas-solid fluidized beds because of their superior heat and mass transfer characteristics compared to other gas-solid contactors. These characteristics are highly impacted by bed hydrodynamics, which, in turn, determined by the magnitudes of interparticle forces (IPFs) and hydrodynamic forces (HDFs). These forces and their relative importance ultimately affect the bed design, scale-up, operation, and overall performance. IPFs significantly affect the bed hydrodynamics in several situations, which are common in catalytic and non-catalytic gas-solid fluidized bed rectors. They include Geldart group A fluidization, where IPFs are in the same order of magnitude as HDFs, Geldart group C fluidization, where IPFs govern hydrodynamic behaviour, and high-temperature operation, where the magnitude of IPFs can significantly be greater than that at ambient conditions. IPFs and their influence on the fluidized bed hydrodynamics are not completely understood since no measurement technique was available to adequately quantify these forces in a gas-solid fluidized bed. This study focuses on developing reliable and non-disrupting approaches for quantifying the magnitude of IPFs in a gas-solid fluidized bed. Incorporating the effect of IPFs into Geldart classification to characterize the boundary between Geldart groups A and C is attempted in the second part of this study.

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
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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