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Record W2034992733 · doi:10.1680/adcr.2008.20.3.121

Numerical studies of mid-kiln tyre combustion

2008· article· en· W2034992733 on OpenAlexafffund
Pirooz Darabi, Jianwei Yuan, M. Salcudean

Bibliographic record

VenueAdvances in Cement Research · 2008
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsProcess Simulations Limited (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKilnCombustorCombustionClinker (cement)Cement kilnRotary kilnWaste managementEnvironmental scienceEngineeringCementNOxAutomotive engineeringNuclear engineeringMaterials scienceMetallurgyPortland cementChemistry

Abstract

fetched live from OpenAlex

A one-dimensional mathematical model of tyre combustion is developed and incorporated in a computational fluid dynamics (CFD) code with the objective of simulating industrial-scale cement kilns and studying the midkiln firing of whole tyres. The simulation without tyre combustion shows a high flame temperature, high NO x emission, and good quality product. Replacing 20% of the total kiln heat with tyres as fuel and dropping them in to the kiln at 50 m from the burner tip results in a reduced flame temperature, a 33% reduction of NO x emission and good clinker quality. Further investigations show that up to 30% replacement of fuel with tyres is acceptable to maintain the clinker quality with reduced NO x emissions. When the tyre replacement is 40% or more, NO x emission can be further reduced, but the clinker quality becomes poor. The tyre drop-in location is another critical parameter. For the kiln studied tyres can be dropped in within 50 m of the burner tip. Further downstream dropping-in reduces kiln efficiency and clinker quality. The actual replacement of tyre combustion and the drop-in location depends on the kiln design and operating conditions. The developed comprehensive simulation tool can be used to reach an optimised kiln design under a specified operating condition.

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 score0.166
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.092
GPT teacher head0.391
Teacher spread0.299 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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