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Record W2794128586 · doi:10.1002/ijch.201700134

CO<sub>2</sub> Conversion Enhancement in a Periodically Operated Sabatier Reactor: Nonlinear Frequency Response Analysis and Simulation‐based Study

2018· article· en· W2794128586 on OpenAlexaff
Robert Currie, Daliborka Nikolić, Menka Petkovska, David S. A. Simakov

Bibliographic record

VenueIsrael Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryNonlinear systemEnergy conversion efficiencyVolumetric flow rateSpace velocityFrequency conversionMechanicsModulation (music)Energy transformationSteady state (chemistry)Bar (unit)Continuous flowControl theory (sociology)Nuclear engineeringAnalytical Chemistry (journal)CatalysisThermodynamicsPhysical chemistryPhysicsSelectivityComputer scienceMeteorologyChromatography

Abstract

fetched live from OpenAlex

Abstract Conversion of CO 2 into synthetic CH 4 via thermocatalytic hydrogenation (the Sabatier reaction), has recently gained increasing interest as a possible route for CO 2 utilization and energy storage pathway. Herein, we analyze the possibility of increasing the CO 2 conversion through periodic operation of the reactor. The analysis is performed by using the Nonlinear Frequency Response (NFR) method, a recently developed analytical technique, suitable for fast evaluation of periodic reactor operations. The NFR analysis predicts a significant conversion gain (up to 50 %) for certain frequencies of the feed flow rate modulation. This prediction is validated by numerical simulations with a reaction rate expression obtained by CO 2 conversion experiments using a Ni/Al 2 O 3 catalysts. Both the NFR analysis and numerical simulations predict that it is possible to obtain 70 % CO 2 conversion at 500 K, 5 bar, and average space velocity of 7600 h −1 by a periodic modulation of the feed flow rate, as compared to the corresponding steady state CO 2 conversion of 43 %.

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 score0.027
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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