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Wood thermodegradation: experimental analysis and modeling of mass loss kinetics

2014· article· es· W2044767795 on OpenAlexaff
Anélie Pétrissans, R. Younsi, Mounir Chaouch, Philippe Gérardin, Mathieu Pétrissans

Bibliographic record

VenueMaderas Ciencia y tecnología · 2014
Typearticle
Languagees
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsService de Recherche et d'EXpertise en Transformation des Produits ForestiersPolytechnique Montréal
FundersAgence Nationale de la Recherche
KeywordsBeechFagus sylvaticaAbies albaPyrolysisInert gasKineticsInertPinus <genus>HardwoodChemistryMaterials sciencePulp and paper industryEnvironmental scienceThermodynamicsBotanyPicea abiesComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, heat treatment was carried out in a relatively low temperature (230˚C). Mass loss kinetics was studied using equipment, specially conceived to measure sample’s mass during the thermal treatment. Laboratory experiments were performed for heating rates of 1˚C min-1. Mathematical model for kinetics of pyrolysis process was used and validated. During the pyrolysis of dry wood samples under inert atmosphere, measurements of temperature distribution and dynamic weight loss were performed. Five different wood species Fagus sylvatica (Beech), Populus nigra (Poplar), Fraxinus excelsior (Ash), Pinus sylvestris (Pine) and Abies pectinata (Silver Fir) were investigated. The unsteady-state mathematical model equations were solved numerically using the commercial package Femlab 2.0. A detailed discussion of the computational model and the solution algorithm is given. The validity of different model assumptions was analyzed. Experimental results were compared with those calculated by the model. Acceptable agreement was achieved.

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 categoriesMeta-epidemiology (narrow)
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 score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.225
Teacher spread0.213 · 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.

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

Citations31
Published2014
Admission routes1
Has abstractyes

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