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Record W4293211597 · doi:10.1002/cjce.24616

Modelling fast pyrolysis of biomass in a fluidized bed reactor

2022· article· en· W4293211597 on OpenAlexvenueno aff
Maurizio Troiano, Valeria Ianzito, Roberto Solimene, Elvis Tinashe Ganda, Piero Salatino

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsElutriationCharFluidized bedPyrolysisBiomass (ecology)tar (computing)Chemical engineeringWaste managementChemistryFluidizationParticle (ecology)Environmental scienceGeologyEngineeringOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Abstract A compartmental one‐dimensional model of a fluidized bed pyrolytic converter of biomass is presented. Reference conditions are those of non‐catalytic fast pyrolysis of biomass in a shallow fluidized bed with external regeneration of the bed material. The fate of biomass and of the resulting char has been modelled by considering elutriation of biomass and char particles, char attrition as well as bed drain/regeneration. The course of primary and secondary pyrolitic reactions is modelled according to a semi‐lumped reaction network using well‐established kinetic parameters taken from the literature. A specific focus of the present study is the role of the heterogeneous volatile–char secondary reactions, whose rate has been modelled by borrowing a kinetic expression from the neighbouring area of tar adsorption/decomposition over char. The results of computations highlight the relevance of heterogeneous volatile–char secondary reactions and of the closely associated control of char loading in the bed. The sensitivity of the reactor performance to char elutriation and attrition, to proper management of bed drain/regeneration, and to control of gas phase backmixing is demonstrated. Model results provide useful guidelines for optimal design and control of fluidized bed pyrolyzers and pinpoint future research priorities.

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 score0.050
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.169
Teacher spread0.160 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

Explore more

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