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Record W1969818335 · doi:10.1002/cjce.5450820317

Simulation and Modeling of Vegetable Market Wastes Pyrolysis Under Progressive Deactivation Condition

2004· article· en· W1969818335 on OpenAlexvenueno aff
Ruby Ray, Pinaki Bhattacharya, Ranjana Chowdhury

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPyrolysisCharBiomass (ecology)Inert gasMaterials scienceInertHomogeneousChemical engineeringThermodynamicsKinetic energyReaction rate constantChemistryKineticsComposite materialOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The pyrolysis behaviour of predried vegetable market waste has been investigated using TGA within the temperature range 523 to 923 K under inert atmosphere and a comparison has been made with other lignocellulosic materials in order to point out the difference between the pyrolysis of nearly homogeneous and perfectly mixed heterogeneous biomass. Kinetic parameters of the pyrolysis material have been evaluated from the simulation of the TG data. A reaction mechanism involving two parallel 1st order reactions evolving gaseous products lumped as volatiles and solid products lumped as char has been proposed for prediction of rate constants as a function of normalized fractional change. Four kinetic models incorporating the effect of deactivation have been used for this purpose. In another attempt, using concentration independent model of solid deactivation, simulation has been carried out to predict concentration time history of the system components as well as quantitative change of rate constants with the propagation of time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.198
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations17
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207