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Record W2023039536 · doi:10.1002/cjce.22182

Effect of fluid velocity on radioactive ion retention by fluidized‐bed reactor

2015· article· en· W2023039536 on OpenAlexvenueno aff
Mohamed Abdelaziz, Azza H. Ali, Hesham Elbakhshawangy, Sameh H. Othman

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionMass transferFluidized bedDiffusionParticle (ecology)MechanicsMaterials scienceVolumetric flow rateFlow (mathematics)Flow velocityColumn (typography)ChemistryThermodynamicsChromatographyMathematicsPhysics

Abstract

fetched live from OpenAlex

This study is concerned with the design, construction, simulation, and pre‐operation of a fluidized‐bed reactor for low level radioactive waste treatment by a new chelating resin. In this respect a new mathematical model is suggested to account for the effect of fluid velocity and to determine the key parameters that affect the overall mass transfer. In general, the column height and the diffusion resistance on the resin side control the extent of separation in the reactor. Lowering the particle falling velocity and increasing the column height increase the adsorption extent. The studied variables include the flow rates, feed concentration, column height, working temperature, and pH of the feed solution. Increasing the initial concentration causes the adsorption to increase and the maximum conversion is obtained at the near‐neutral condition. Temperature increasing, on the other hand, decreases the adsorption extent. The theoretically calculated results from the mathematical model developed in this study for predicting the conversion extent are found to be in good agreement with the data obtained from the experimental study.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.211
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

Citations3
Published2015
Admission routes1
Has abstractyes

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