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Record W2026885981 · doi:10.2202/1542-6580.1428

Catalyst-Adsorbent Configurations in Enhancing Adsorptive Reactor Performance

2007· article· en· W2026885981 on OpenAlexfundno aff
Saleh Rawadieh, Vincent G. Gomes

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsCatalysisPropenePressure swing adsorptionSeparator (oil production)AdsorptionProduct distributionMass transferChemical engineeringYield (engineering)Materials scienceSelectivityChemistryChromatographyThermodynamicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Fixed-bed columns containing solid catalysts and adsorbents were employed for simultaneous reaction and separation. The models developed for propene metathesis were validated with experimental data for configurations with and without the effect of pressure swing reaction. The models for the mass and momentum transfer in the catalyst bed and adsorber were solved using orthogonal collocation within the method of lines. The reactor/separator performances were tested for beds with varying numbers of layers of catalysts and adsorbents which were arranged sequentially. The pressure reaction columns behaved as reactor/separators in series. As the number of layers increased, a homogeneous distribution of the catalyst and adsorbent was approached in the limit. These configurations with variable catalyst/adsorbent distributions were investigated for propene metathesis reaction, in terms of product purity, selectivity, conversion, recovery, yield and productivity. Improved reactor performance was observed with a pressure swing separation system and in particular with close to well-mixed reactor/separator configurations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.600

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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