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Record W1965663050 · doi:10.1021/ie901710n

Biomass Catalytic Steam Gasification Thermodynamics Analysis and Reaction Experiments in a CREC Riser Simulator

2010· article· en· W1965663050 on OpenAlexaff
Enrique Salaices, Benito Serrano, Hugo de Lasa

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsBiomass (ecology)Wood gas generatorChemical equilibriumThermodynamicsThermodynamic equilibriumChemistryProduct distributionNon-equilibrium thermodynamicsCatalysisProcess engineeringOrganic chemistryPhysicsEngineeringGeology

Abstract

fetched live from OpenAlex

A thermodynamic equilibrium model based on evaluations involving C, H, and O element balances and various product species up to C 6 hydrocarbons is reported in this study. This model establishes the effects of biomass composition, temperature, and steam on the various gas product molar fractions. On this basis, most significant parameters determining the chemical interconversions and distribution of chemical species are identified. On the basis of the proposed equilibrium model and using glucose as a model biomass species [C 6 H 12 O 6 ], an optimum gasification temperature close to 800 °C and a steam/biomass ratio between 0.5 and 0.7 g/g is established. This study has the special value of comparing thermodynamic equilibrium predictions with experimental data obtained in a CREC riser simulator using a fluidizable Ni−Al 2 O 3 catalyst. Results are relevant for scaled-up gasifiers. They show that for reaction times longer than 30 s chemical species are essentially equilibrated and that the proposed model does provide adequate description of various product fractions. Data obtained also demonstrate the shortcomings of equilibrium models for gasifiers with reaction times shorter than 10 s and the need for nonequilibrium models to describe gasifier performance at such conditions.

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 score0.059
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.303
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

Citations40
Published2010
Admission routes1
Has abstractyes

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