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Record W4323975086 · doi:10.1016/j.fuel.2023.127947

Conversion of CO2 by reverse water gas shift (RWGS) reaction using a hydrogen oxyflame

2023· article· en· W4323975086 on OpenAlexaff
Ali Shekari, Raynald Labrecque, Germain Larocque, Michel Vienneau, Martin Simoneau, Robert Schulz

Bibliographic record

VenueFuel · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsSyngasWater-gas shift reactionProcess engineeringResidence time (fluid dynamics)HydrogenOperating temperatureFossil fuelEnvironmental scienceGreenhouse gasMaterials scienceChemical engineeringSyngas to gasoline plusNuclear engineeringSteam reformingThermodynamicsChemistryHydrogen productionOrganic chemistry

Abstract

fetched live from OpenAlex

Climate change concerns demand for drastic measures to mitigate greenhouse gas (GHG) emissions from fossil resources particularly those of CO2. Effective conversion of CO2 into syngas (a mixture of CO and H2) via reverse water gas shift (RWGS) reaction requires high temperatures (> 900 °C) to overcome thermodynamic limitations. Challenges may arise in commercial catalytic reactors in achieving close to equilibrium efficiencies due to physical barriers such as maximum catalyst operating temperature and associated operating costs. Herein, a simple and novel approach is presented to produce syngas from CO2 via RWGS reaction using a high temperature hydrogen oxyflame. Test results from a tubular laboratory reactor show that a CO2 conversion of up to 75 % is achievable in a single pass with a gas residence time of < 0.03 s. Lower than equilibrium CO2 conversions are observed due to non-adiabatic temperatures in the reactor. Heat integration and reactor insulation at industrial scale would help in a closer to equilibrium performance. Systematic analysis of reactor performance supported by empirical modelling revealed that there is an optimum economical point where hydrogen consumption can be minimized (< 1.0 mol H2/mol CO2). A trade-off must be made to identify an optimum operating point depending on required syngas quality. Relatively small effect of reactor wall material (within 10 % in CO2 conversion) may indicate an enhancing effect of hydrogen oxyflame as the main contributor to the reactor performance.

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 score0.018
Threshold uncertainty score0.396

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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