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Record W2071574712 · doi:10.1002/cjce.20080

Auto‐thermal combustion of lean gaseous fuels utilizing a recuperative annular double‐layer catalytic converter

2008· article· en· W2071574712 on OpenAlexvenueno aff
Wojciech M. Budzianowski, Ryszard Miller

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsOverheating (electricity)CombustionCatalytic combustionCatalytic converterMass transferCatalysisMechanicsAdiabatic processMaterials scienceTransient (computer programming)ThermodynamicsChemistryNuclear engineeringPhysicsEngineeringComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The results of the investigations on the auto‐thermal combustion of lean gaseous fuels in a recuperative annular double‐layer catalytic converter were reported in the current contribution. Several modifications were proposed to improve the stationary and transient behaviour of the converters. The miniaturized recuperative converter exhibited reduced resistances to the mass and heat transfer and attractive bifurcation changes of a very low combustible content, that is, the histeresis for T in and C in and isola for mf in and h g . It was revealed that the utilization of an adiabatic recuperative converter led to an autothermal operation for T in = 300 K and C in = 177 ppmv of propane. The inlet fuel mass flow rate range to apply was wider than earlier reported in the literature, that is, 0.63–2.94 × 10 −6 kg s −1 for C in = 200 ppmv. Transient experiments showed that recuperative converter was able to transfer short‐time inlet disturbances of parameters due to the energy accumulation and temporal reversed recuperation counteracting to extinction or to destructive overheating of the catalysts. Stability analysis was performed showing location of folds, stable and unstable branches of solutions for the different parameters of the recuperative converter. A two‐dimensional process model was developed.

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)0.000
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 score0.001
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.220
Teacher spread0.199 · 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

Citations33
Published2008
Admission routes1
Has abstractyes

Explore more

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