Detection of Injected Urea Quantity and Correction for SCR Urea Dosing Control
Bibliographic record
Abstract
Using urea-based Selected Catalytic Reduction (SCR) systems is an effective way in diesel engine after-treatment systems to meet increasingly stringent emission regulations. The amount of urea injection is critical to achieve high NOx reduction efficiency and low ammonia slip and overdosing or under-dosing of urea injection need to be avoided. One of the difficulties in urea injection amount control lies in the accurate measurement/estimation of the urea injection mass. To effectively address this issue, this paper defined a correction factor for under-dosing or overdosing detection and correction and proposed two methods to identify the correction factor. The first method is based on urea pump model and line pressure. Through frequency analysis, the relation between the urea pump speed and power spectrum characteristics of the line pressure by using FFT method was revealed. The urea pump frequency is extracted from the frequency analysis of the line pressure and used for detecting the urea injection and identifying the correction factor. Second method is based on the effective voltage of the Motor. The relation between urea injection and effective voltage of the Motor was revealed based on the motor dynamic model. The effective voltage of the Motor is used to detect the urea injection and identify the correction factor. The effectiveness of both proposed detection and correction methods are experimentally validated through the urea dosing system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".