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Record W2899536201

Development of a reactor-heat exchanger of monolith type for three-phase hydrogenation reactions: proof of concept and modelling strategy

2017· preprint· en· W2899536201 on OpenAlexaff
Freddy-Libardo Durán Martínez, Carine Julcour‐Lebigue, Faı̈çal Larachi, Pierre Alphonse, Anne‐Marie Billet

Bibliographic record

VenueOpen Archive Toulouse Archive Ouverte (University of Toulouse) · 2017
Typepreprint
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMonolithCatalysisMaterials scienceChemical engineeringMicroreactorAutoclaveReaction rateHeat exchangerCoatingIsothermal processChemical kineticsChemistryKineticsComposite materialThermodynamicsOrganic chemistryMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

This study aimed at developing an innovative catalytic reactor inspired from monolith technology and equipped with in situ heat removal system. To illustrate the approach, the total hydrogenation of a bio-sourced olefin, alpha-pinene, was chosen as a model reaction. Preliminary thermal calculations proved the proposed reactor-heat exchanger configuration to be uniform in temperature, so that its behavior could be described through that of a single isothermal channel. The developed methodology then included the elaboration of the catalytic coating, the investigation of the reaction kinetics, the activity assessment of the catalytic capillary tube at pilot scale and the scale-up of the monolith reactor through multiscale and multiphysics modelling. 
\nFirst part of the work was dedicated to the catalytic tests, operated in a batch stirred autoclave reactor using both small coated platelets and powdered catalyst. Catalyst formulation and synthesis method were varied, leading to an efficient Pd/Al2O3 coating: it yielded a well adherent deposit of 5-10 µm thickness on aluminum alloy processed by selective laser melting and its initial activity exceeded that of a commercial egg-shell catalyst by more than one order of magnitude due to high metal dispersion (Pd nanoparticles of ca. 1 nm). The intrinsic kinetics of the reaction was investigated using the catalyst in powdered form: an overall activation energy of about 40 kJ/mol was obtained, and a Langmuir-Hinshelwood rate law with surface reaction as rate-determining step adequally described complex reaction orders with respect to alpha-pinene and hydrogen.
\n \t This selected catalyst was then coated on a series of jacketed aluminium tubes of 2 mm internal diameter and 40 cm total height. Their activity was assessed on a continuous hydrogenation set-up operating in the Taylor flow regime at 10-20 bar and 100-160°C. The capillary reactor was modelled through a “Unit Cell” approach(gas bubble surrounded by a liquid film and separated by two liquid half-slugs) accounting for hydrodynamics, gas-liquid and liquid-solid mass transfer and complex reaction kinetics. Numerical simulation of Unit Cell hydrodynamics was first thoroughly questioned, by examining the merits of simplifying hypotheses regarding the bubble shape to be considered, as well as the equations and boundary conditions to be solved. It served as a support for the transient calculation of the reactant concentrations, that mimicked the progression of the Unit Cell along the reactor. In addition to the effects of operating parameters on pinene conversion, those of gas consumption along the reactor (hydrogen being here the limiting reactant), "initial" saturation conditions of the liquid and catalyst activity were analyzed thanks to the numerical model. It was also used to evaluate a more direct reactor sizing tool, based on plug flow behavior and overall exchange coefficients. The latter were calculated either from existing correlations or from the numerical simulations, by evaluating the separate contributions of different parts of the bubble surface (film and caps) to gas-liquid mass transfer and the concentration gradients near the reactor wall.
\nFinally, the behavior of the entire monolith could be reproduced from this model by combining, in a mixing module at the reactor outlet, the liquid outflows of channels whose individual flow rates matched the fluid distribution measured on a cold mock-up of the reactor-heat exchanger.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.068
GPT teacher head0.273
Teacher spread0.205 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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