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Record W1980491227 · doi:10.1002/cjce.20110

Notes on the issues of equilibrium in the Fischer–Tropsch synthesis

2008· article· en· W1980491227 on OpenAlexaffvenue
Graeme W. Norval

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFischer–Tropsch processProduct distributionChemistryThermodynamicsEquilibrium constantCatalysisChemical equilibriumProduct (mathematics)Phase equilibriumHydrocarbonKinetic energyThermodynamic equilibriumDistribution (mathematics)Phase (matter)Physical chemistryOrganic chemistryMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract The product distribution for the Fischer–Tropsch synthesis is normally described using the kinetically derived (Anderson–Schultz–Flory) ASF model. Variations of the kinetic model have been proposed to explain deviations from the ASF distribution. The Fischer–Tropsch system can be equally well described using a pseudo‐element (CH 2 , H 2 , O) equilibrium approach. A one‐parameter equilibrium model is derived for the product distributions for alkenes, alkanes and alcohols. The Fischer–Tropsch system should be considered as three separate partial equilibria systems: the product homologous series; the water gas shift system, and the redox behaviour of the catalyst with the H 2 /O ratio of the gas. This approach correctly predicts the impacts of changes in a variety of parameters (temperature pressure, feed composition) on the ASF product distribution. In addition, the catalyst phase changes with gas composition and pressure, indicative of an equilibrium response. Equilibrium is of much greater importance to the Fischer–Tropsch system than previously thought, and the decision to use a complex kinetics‐based model rather than a simpler equilibrium based model should be taken with care.

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)0.003
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 score0.023
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.021
GPT teacher head0.216
Teacher spread0.195 · 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

Citations14
Published2008
Admission routes2
Has abstractyes

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