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Record W2389895247

An improved kinetic model for cellulose pyrolysis

2002· article· en· W2389895247 on OpenAlexaff
Yu Chun

Bibliographic record

VenueJournal of Zhejiang University(Engineering Science) · 2002
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCellulosePyrolysisKinetic energyKineticsBiomass (ecology)ChemistryThermodynamicsChemical engineeringChemical kineticsMaterials scienceOrganic chemistryEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Cellulose pyrolysis is an important area in biomass thermochemical conversion. Developing advanced pyrolysis technology requires basic understanding of chemical kinetics. This paper deals with the improvement of the kinetic model for cellulose pyrolysis. To study this process, intensive reviews, calculations, comparisons and analyses were carried out based on the well known Broido Shafizadeh kinetic model. An improved kinetic model of cellulose pyrolysis was proposed. First, the validity of the kinetic data on competitive reactions in Broido Shafizadeh model was checked. Through analyses and comparisons, a new kinetics model was developed to overcome the error caused by heat and mass transfer limitation in the parameter deduced experiment. Then, the so called 'active cellulose' was examined. The appearance of liquid 'active cellulose' during pyrolysis was analyzed on the basis of modelcalculation and shown to be important in the case of high heating rate pyrolysis processes and should be considered in the improved kinetic model. At last, the secondary reaction of volatile components was added as a part of the kinetic model. Relevant kinetic data were chosen from literature. The proposed kinetic model for cellulose pyrolysis is a step toward the reality and will certainly benefit further study on cellulose pyrolysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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