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Record W2514725691 · doi:10.1016/j.ifacol.2016.08.002

Estimating tailpipe NOx concentration using a dynamic NOx/ammonia cross sensitivity model coupled to a three state control oriented SCR model

2016· article· en· W2514725691 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIFAC-PapersOnLine · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNOxSelective catalytic reductionDiesel engineDiesel fuelSlip (aerodynamics)Sensitivity (control systems)AmmoniaEnvironmental scienceAutomotive engineeringCatalysisChemistryEngineeringCombustion

Abstract

fetched live from OpenAlex

A dynamic NOx sensor model is developed to remove ammonia cross sensitivity from production NOx 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 NH3 concentration downstream of the SCR (NH3 slip). NH3 slip is then used as an input for modeling the cross sensitivity of a production NOx sensor and calculating the actual NOx concentration in the presence of NH3 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 NOx concentration downstream of the SCR. This model should be of utility for engine emission control strategies such as SCR control.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.298
Teacher spread0.281 · 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