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Record W2007444918 · doi:10.1021/ie302690p

Catalytic Wet Oxidation in Three-Phase Moving-Bed Reactors: Modeling Framework and Simulations for On-Stream Replacement of a Deactivating Catalyst

2012· article· en· W2007444918 on OpenAlexaff
Ion Iliuta, Faı̈çal Larachi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCatalysisResidence time (fluid dynamics)Steady state (chemistry)Chemical engineeringChemistryWaste managementPhase (matter)Chemical reactorMaterials scienceProcess engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Phenol wet oxidation over deactivating catalysts in three-phase moving-bed reactors was simulated by formulating and solving a two-scale, nonisothermal, non-steady-state model to highlight the strength of on-stream catalyst replacement, in comparison to catalyst-batch fixed-bed reactors. Simulation results indicate that three-phase moving-bed reactors offer a promising alternative to fixed-bed reactors. The autonomy of fixed-bed reactors is limited due to severe reduction of catalyst activity, while in moving-bed reactor configurations, the decline of pollutant conversion is reduced with increased solid velocity, to compensate for the decrease in catalyst activity loss. The fixed-bed reactor operates in non-steady-state mode, because of the continuous decline of catalyst activity while moving-bed reactors evolve to steady-state operation after a transient period. Decreasing the reactor feed phenol concentration and increasing liquid residence time in the reactor and feed temperature are the best ways to oppose rapid deactivation of catalyst in moving-bed reactors.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.112
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.116
GPT teacher head0.384
Teacher spread0.268 · 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.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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