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Record W2320855887 · doi:10.1021/ef301887f

Predictive Kinetics Model for an Industrial Waste Tire Pyrolysis Process

2013· article· en· W2320855887 on OpenAlexafffund
Jean-Rémi Lanteigne, Jean‐Philippe Laviolette, Gilles Tremblay, Jamal Chaouki

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsPolytechnique Montréal
FundersMitacs
KeywordsPyrolysisCharKineticsThermogravimetric analysisYield (engineering)DecompositionProcess (computing)Process engineeringMaterials scienceThermodynamicsChemistryComputer scienceOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

A new pyrolysis model was developed to predict the individual product (noncondensable volatiles, condensable volatiles, and char) yield for Ecolomondo’s industrial waste tire pyrolysis process. This novel predictive kinetics-based model couples product selectivity data obtained from thermogravimetric analysis experiments to a global single-step decomposition reaction term to reproduce the nonlinear relationship between product selectivity and temperature. A transient energy balance based on a lumped capacitance method was also used to calculate the tire shred temperature using the rotary drum wall temperature as an input. The kinetics model was compared to experimental oil production data from the industrial process as well as existing models in the literature. It is shown that the model can successfully predict the oil production of the industrial process and the model accuracy is greater for smooth operating conditions. On the other hand, other pyrolysis models from the literature failed to accurately predict the oil production.

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 score0.202
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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

Citations11
Published2013
Admission routes2
Has abstractyes

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