JV Task-Long-Kiln NOx Reduction Study
Bibliographic record
Abstract
Field sampling was conducted by the Energy & Environmental Research Center at two Lafarge North America cement kiln locations in Canada. Emissions including SO{sub x}, NO{sub x}, and particulate were measured and reported at various locations throughout the kilns. At each site data were collected on two kilns during field sampling. However, only Kiln 1 at the Ravena site was utilized for modeling efforts. Experimental work was then conducted to estimate the effectiveness of various NO{sub x} control techniques on limiting both NO{sub x} and SO{sub x} emissions in cement kiln exhaust. Theory-based models were constructed to estimate both NO{sub x} and SO{sub x} emissions from cement kilns. These models were then applied to estimating the impact of various NO{sub x} control strategies on kiln exhaust emissions. The sulfur model constructed as part of this work was successful at predicting SO{sub 2} emissions and sulfur capture in the Alpena kiln. This model is designed to run as a postprocessing step that uses the output of a NO{sub x} model as input. With an accurate NO{sub x} model, the sulfur model may prove to be a valuable tool in estimating the impact of kiln modifications on sulfur emissions. The NO{sub x} model was also applied to model several operating scenarios on three of Lafarge's kilns: Alpena 20/21, Alpena 22/23, and Ravena 1. The predictions of the flue gas temperature at the kiln feed end, the kiln shell heat loss, the quality of clinker, and the excess O{sub 2} in the flue gas are consistent with the audit data. The developed simulation tool in this project has proven to be an effective way to investigate the NO{sub x} emissions, to optimize kiln performance, and to assess changes in operating condition on kiln performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".