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Record W2059366968 · doi:10.1021/ie8013605

Determination of Distributed Activation Energy Model Kinetic Parameters Using Simulated Annealing Optimization Method for Nonisothermal Pyrolysis of Lignin

2008· article· en· W2059366968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPyrolysisActivation energyLigninKinetic energyThermogravimetric analysisThermodynamicsMaterials scienceAnnealing (glass)ChemistryPhysical chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The distributed activation energy model (DAEM) has been shown to be more descriptive of the pyrolysis reaction than other applicable models. In this study, the temperature dependency of the preexponential factor has been included in the DAEM. The model equation has been solved using Simpson’s 1/3 rule, and the kinetic parameters were determined using an optimization method. Simulated annealing method has been used to determine the DAEM kinetic parameters for the nonisothermal pyrolysis of lignin using thermogravimetric analysis (TGA) data. The nonisothermal pyrolysis of lignin was conducted at three different heating rates of 5, 10, and 15 °C/min under nitrogen atmosphere. Predicted results from the optimum kinetic parameters have been compared with the experimental data. The DAEM equation predicts the experimental data very well for different heating rates.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.112
GPT teacher head0.341
Teacher spread0.230 · 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