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Record W2076635484 · doi:10.1115/fbc2003-053

Combustion Characteristics of Natural Gas in a Circulating Fluidized Bed

2003· article· en· W2076635484 on OpenAlexaff
Dennis Y. Lu, Edward J. Anthony

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCombustionFluidized bed combustionCharChemical looping combustionFluidized bedNOxWaste managementNatural gasChemistryEnvironmental scienceChemical engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Recently there has been interest in extending the application of fluidized bed combustors (FBCs) to fuels with difficult handling properties or ones that are associated with non-conventional air pollutant problems. These fuels, such as biomass, plastic wastes, black liquors and heavy liquid fuels, have very high volatiles contents and, because they are often treated as easily-burned materials, they have received much less attention than has been given say to the combustion processes for char in FBCs. Understanding their gas-phase chemistry is helpful in optimizing their combustion. This paper describes the study of natural gas combustion in a fluidized bed as a simple model for studying gas-phase reactions involving C/H/N/O chemistry in the absence of char. The experimental work was conducted using a pilot-scale CFBC unit. Combustion characteristics and emissions were investigated by varying the operating conditions and in particular the combustion temperature, fluidizing velocity and bed material. The results indicated that fluidized bed combustion chemistry is associated with superequilibrium free radical processes, similar to high-temperature flame systems. In this system, prompt-NO mechanisms are the only routes for NO formation and this work shows that they can lead to significant NOx production.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.304

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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 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

Citations1
Published2003
Admission routes1
Has abstractyes

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