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Record W2526616904 · doi:10.1002/aic.15526

Three‐dimensional modeling of porosity development during the gasification of a char particle

2016· article· en· W2526616904 on OpenAlexaff
Kay Wittig, Petr A. Nikrityuk, S. Schulze, Andreas Richter

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCharPorosityParticle (ecology)Context (archaeology)Work (physics)Materials scienceMechanicsParticle sizeChemical engineeringThermodynamicsPhysicsComposite materialEngineeringGeologyPyrolysis

Abstract

fetched live from OpenAlex

This work is devoted to the three‐dimensional, direct modeling of porosity and specific surface development during the gasification of a char particle. The model was developed for heterogeneous reactions occurring inside a char particle in a kinetically controlled regime. The main goal of this work is to analyze the impact of different pore size distributions on the particle carbon conversion rate. In particular, it is shown that under certain conditions the outer particle surface can influence the specific surface area. In this context the possible adaptation of the parameter ψ from the random pore model (RPM) developed by Bhatia and Perlmutter is explained. The results of simulations are compared against the RPM and discussed. Additionally, based on the results of simulations, the physics behind several input parameters used by the RPM are explored. Finally, the possible fragmentation of a chemically reacting char particle during its gasification in dependence of instantaneous porosity was investigated numerically. It was shown that the earliest fragmentation occurs at a carbon conversion of about 0.5–0.6 due to the disaggregation of the pore walls. The results are discussed and compared implicitly with data published in the literature. © 2016 American Institute of Chemical Engineers AIChE J , 63: 1638–1647, 2017

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 score0.007
Threshold uncertainty score0.136

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 head0.015
GPT teacher head0.195
Teacher spread0.181 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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