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Effects of Metallic Heating Plates on Coal Pyrolysis Behavior in a Fixed-Bed Reactor Enhanced with Internals

2017· article· en· W2591022515 on OpenAlexaff
Xi Zeng, Fang Wang, Yuan Li, Xiaojian Yi, Xiaoheng Fu

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of New Brunswick
FundersNational High-tech Research and Development ProgramMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsPyrolysisCoaltar (computing)Dry distillationMaterials scienceYield (engineering)CrackingCoal tarChemical engineeringMetalCharChemistryDestructive distillationComposite materialMetallurgyCarbonizationOrganic chemistryScanning electron microscope

Abstract

fetched live from OpenAlex

A newly configured fixed-bed reactor with internals has been proposed to enhance the coal pyrolysis performance. In this study, the effects of metallic plates on coal pyrolysis behavior were investigated in this reactor. The results show that the increased quantity of the metallic plates enhanced the heat transfer and shortened the residence time of volatiles within the coal particles. In addition, the pressure drop results suggest that the increased quantity of plates caused short circuiting of gas and raised the particle interstices, which reduced the gas diffusion resistance of pyrolysis products. Therefore, more gaseous pyrolysis products flowed into central low-temperature coal bed and escaped from the gas collection pipe, suppressing the secondary reaction of pyrolysis products and increasing the tar yield and quality. At a furnace temperature of 900 °C, the increase in metallic plates from 0 to 8 raised the tar yield and light tar fraction from 5.20 and 69.5 wt % to 7.86 and 77.0 wt %, respectively. Meanwhile, the ≤C 14 hydrocarbons were elevated from 42.11 to 50.44 wt %, but the ≥C 20 hydrocarbons were lowered from 29.95 to 19.74 wt %. However, an excessive increase in plates raised the heating rate of coal, cracking more pyrolysis products and decreasing the tar yield and quality.

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 score0.004
Threshold uncertainty score0.610

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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