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Record W7094266579

Simulated moving bed reactor for p-xylene production: Optimal particle size

2015· article· en· W7094266579 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Repository of the University of Porto (University of Porto) · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsnot available
Fundersnot available
KeywordsSimulated moving bedRaffinateParticle (ecology)Pressure dropWork (physics)Particle sizeYield (engineering)
DOInot available

Abstract

fetched live from OpenAlex

In a previous work, a hybrid unit combining separation and isomerization within a Simulated Moving Bed Reactor unit (based on an existing Simulated Moving Bed) for the production of p-xylene was analyzed with the currently-used particle diameter of 0.62mm. The unit is part of a proposed aromatics complex where lower p-xylene purities allow the use of a homogeneous mixture of adsorbents and catalysts within the columns. In this work a similar study is carried out with four particle diameters: 0.5, 0.7, 0.8, and 0.9mm, maintaining the extract and raffinate purity at 0.70 and 0.95 respectively, and a mass ratio of 0.9g adsorbent per g of adsorbent-plus-catalyst. After performing simulations using the true moving bed approach, it is verified that the high amount of desorbent is mainly caused by the reverse reaction in the isomerization of xylenes. Furthermore, the highest productivity is offered by the 2-6-14-2 configuration for every particle size studied. The system is then analyzed with that arrangement of columns under the maximum pressure drop of the existing Simulated Moving Bed unit (685kPa). The optimal particle diameter is 0.62mm, exhibiting the highest productivity (267.40kg/m3·h), and a desorbent consumption of 0.06 m3/kg. The results also show that a single study with a small particle size is sufficient to accurately determine the best configuration for the system. (c) 2015 Canadian Society for Chemical Engineering.

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 score0.074
Threshold uncertainty score0.444

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 science0.0010.000
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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueOpen Repository of the University of Porto (University of Porto)Same topicProtein purification and stabilityFrench-language works237,207