Numerical studies of mid-kiln tyre combustion
Bibliographic record
Abstract
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 NOx 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 NOx 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 NOx emissions. When the tyre replacement is 40% or more, NOx 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".