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Record W1499701224

PREDICTION OF CATALYST ATTRITION IN AN INDUSTRIAL FLUIDIZED BED PLANT BASED ON LAB SCALE ATTRITION TESTS

2010· article· en· W1499701224 on OpenAlexaboutno aff
Ernst‐Ulrich Hartge, Stefan Heinrich, Joachim Werther, Anja Püttmann, Andreas Thon, Gregory S. Patience, Richard E. Bockrath

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2010
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionFluidized bedPilot plantCatalysisProcess engineeringScale (ratio)Process (computing)Maleic anhydrideButaneEnvironmental scienceEngineeringWaste managementMaterials scienceChemistryComputer scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

The concept that catalyst attrition in a fluidized bed process can be simulated based on the attrition characteristics of the catalyst determined on labscale is applied to DuPont’s large scale process of maleic anhydride production from n-butane using a vanadium phosphorus oxide (VPO) catalyst. In the present cooperation effort the performance data and design parameters of the large scale plant are supplied by the Ecole Polytechnique de Montreal and DuPont. At Hamburg University of Technology the catalyst’s attrition characteristics were determined in labscale attrition tests. Furthermore the fate of the solids in the process was simulated. The results of the simulation are compared with the performance data of the industrial process.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.212
Teacher spread0.193 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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