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Record W1577401329

TIME-SCALE ANALYSIS OF A FLUIDIZED-BED CATALYTIC REACTOR BASED ON A GENERALIZED DYNAMIC MODEL

2007· article· en· W1577401329 on OpenAlexaff
Andrés Mahecha‐Botero, John R. Grace, S.S.E.H. Elnashaie, C. Jim Lim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluidized bedMass transferMechanicsResidence time (fluid dynamics)Chemical reactorChemistryThermodynamicsProcess engineeringEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

A fluidized-bed reactor model is implemented to simulate a Maleic Anhydride (MA) reactor with special emphasis on its dynamic behaviour. The dynamic model is general enough that it can treat a wide range of catalytic systems, subject to mass and energy balances within the phases. The model represents multiple phases and regions (low-density phase, high-density phase, freeboard region) and can account for heat and mass axial and radial anisotropic dispersion, change in molar/volumetric flow due to reaction, temperature and pressure profiles, hydrodynamic regime variation, catalyst deactivation, energy options, and multiple membranes of various geometries for introduction/extraction of any compound. The model reduces, as special cases, to most fluidized bed reactor models reported in the literature, allowing the influence of simplifying assumptions to be investigated. Introduction of different assumptions for a MA fluidized-bed reactor of industrial scale reveal quite different predicted time scales for key dynamic phenomena inherent to the process. A mass transfer/reaction time scale was found to be close to the residence time of the gas molecules in the reactor. The heat transfer time scale is several orders of magnitude larger for the current system. This type of time-scale analysis may be a useful tool to identify the appropriate degree of sophistication to predict the dynamics of complex reacting systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.218
Teacher spread0.212 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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