MétaCan
Menu
Back to cohort

Effect of the Moisture Content in Coal on the Pyrolysis Behavior in an Indirectly Heated Fixed-Bed Reactor with Internals

2017· article· en· W2581858693 on OpenAlexaff
Xi Zeng, Dachao Ma, Fang Wang, Xiaojian Yi, Yuan Li, 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 ProgramChina Scholarship CouncilMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsCharPyrolysisCoaltar (computing)Water contentMoistureYield (engineering)Carbon fibersMaterials scienceCoal tarHeat of combustionPulp and paper industryDehydrationChemical engineeringCondensationChemistryWaste managementComposite materialOrganic chemistryThermodynamicsGeologyCombustion

Abstract

fetched live from OpenAlex

The fixed-bed reactor with internals has been proposed to enhance the pyrolysis performance for coal. In this study, the pyrolysis behavior of different coal moisture contents and the reaction mechanism were investigated in an indirectly heated fixed-bed reactor with internals. The results showed that, at a furnace temperature of 900 °C, the increased coal moisture content went from 0.41 to 11.68 wt % and significantly modified the temperature fields, thereby prolonging the pyrolysis time to reach 500 °C and then enhancing the condensation and trapping of the coal at the bed center. Therefore, the tar yield and light tar content were raised from 9.21 and 63.7 wt % to 10.74 and 64.5 wt %, respectively. However, when the coal moisture content exceeded 16.77 wt %, the tar yield and light tar content decreased to 8.55 and 62.0 wt %, respectively. In addition, the higher heating value (HHV) of char with internals was dramatically higher than that without internals, and the char HHV in the reactor with internals rose primarily and then decreased with the increase in the coal moisture; meanwhile, its fixed carbon content of char showed an increase, followed by a decline. In contrast, the pyrolysis products varied slightly in the reactor without internals.

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 score0.012
Threshold uncertainty score0.351

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 science0.0010.000
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207