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Record W2620773488 · doi:10.1002/cjce.22907

Study on the combustion and NO<sub>x</sub> emission characteristics of low rank coal in a circulating fluidized bed with post‐combustion

2017· article· en· W2620773488 on OpenAlexvenueno aff
Tuo Zhou, Qinggang Lu, Yong Cao, Guanglong Wu, Shiyuan Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsFluidized bed combustionCombustionFlue gasCoalCoal combustion productsWaste managementFluidized bedMaterials scienceFly ashNitrogenEnvironmental scienceChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The combustion and NOx emission characteristics of low rank coal have been investigated in a circulating fluidized bed test plat, which was composed of a circulating fluidized bed (CFB) and post‐combustion chamber (PCC). The effects of the air stoichiometric ratio in the circulating fluidized bed furnace and air arrangement in the post‐combustion chamber were studied. In the optimized experimental condition, with the air stoichiometric ratio in the CFB (λCFB) at 0.963, and post‐combustion air (PCA) injected into the PCC using ports PCA1 and PCA3, the NOx emission was reduced to 48.7 mg/m3. The conversion of the fuel‐nitrogen in the coal into NOx did not occur under the reducing atmosphere in the circulating fluidized bed furnace. The final quantity of NOx generated from the combustion in the PCC was formed from gaseous HCN and residual nitrogen in fly ash, which flowed with flue gas from the CFB furnace. Enhancing the burnout fraction of coal in the CFB and using the air arrangement in the PCC could effectively reduce the final emission of NOx.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.186
Teacher spread0.175 · 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 designObservational
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

Citations23
Published2017
Admission routes1
Has abstractyes

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