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Record W2102111394 · doi:10.1002/cjce.20151

Simulation of coal gasification in a pressurized spout‐fluid bed gasifier

2009· article· en· W2102111394 on OpenAlexvenueno aff
Qianjun Li, Zhang Mingyao, Wenqi Zhong, Xiaofang Wang, Rui Xiao, Baosheng Jin

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsWood gas generatorFluidizationCoalCoal gasificationHeat transferMass transferPyrolysisWaste managementMaterials scienceFluidized bedNuclear engineeringMechanicsPetroleum engineeringEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Based on an Eulerian–Eulerian method, a three‐dimensional kinetic model involving mass transfer, momentum transfer, heat transfer, and chemical reaction is developed to simulate the process of coal gasification in a 2 MWth pressurized spout‐fluid bed of 450 mm in diameter with bed pressure up to 0.5 MPa. The effects of operating pressure and bed temperature on coal gasification are investigated. The high operating pressure is beneficial to coal gasification due to the fact that the fluidization in the reactor becomes better. On one hand, a higher bed temperature can accelerate the rate of reaction. On the other hand, more air will be taken in the gasifier to keep the higher bed temperature which will consume part of combustible gases produced by coal pyrolysis or gasification. Experimental verification was carried out in a 2 MWth thermal input pressurized spout‐fluid bed under the same operating condition. The comparison of calculation results with experimental results shows that most of the calculation errors are within the range of 15%.

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 score0.040
Threshold uncertainty score0.080

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 head0.194
Teacher spread0.185 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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