Estimating tailpipe NOx concentration using a dynamic NOx/ammonia cross sensitivity model coupled to a three state control oriented SCR model
Bibliographic record
Abstract
A dynamic NO x sensor model is developed to remove ammonia cross sensitivity from production NO x sensors mounted downstream of Diesel-engine selective catalytic reduction (SCR) systems. The model is validated for large amounts of ammonia slip during different engine transients. A three-state nonlinear control oriented SCR model is also developed to predict the NH 3 concentration downstream of the SCR ( NH 3 slip). NH 3 slip is then used as an input for modeling the cross sensitivity of a production NO x sensor and calculating the actual NO x concentration in the presence of NH 3 contamination. The cross sensitivity is considered to be a function of temperature, normalized ammonia slip rate (NASR) and time. The validation results show that the developed model has an acceptable accuracy for the actual NO x concentration downstream of the SCR. This model should be of utility for engine emission control strategies such as SCR control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".