Hydrothermal Aging Factor Estimation for Two-Cell Diesel-Engine SCR Systems via a Dual Time-Scale Unscented Kalman Filter
Bibliographic record
Abstract
Selective catalytic reduction (SCR) systems have been widely used to meet the emission regulations and two-cell SCR systems have shown the advantages of high NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> conversion efficiency and low NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> slip simultaneously. However, it is noteworthy that the catalyst performance of SCR device would degrade over the service time gradually. If the performance degrading is not well-compensated, the NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> conversion efficiency would reduce significantly. Different from detecting the aging issue in the laboratory, a practical method is to design an observer to estimate the aging factor online such that the urea injection can be modified accordingly. In this paper, we aim to construct an aging-factor observer for two-cell SCR systems. In order to reduce the algorithm computational load and guarantee the implementation performance, we propose a dual time-scale algorithm based on two-cell SCR model and unscented Kalman filter. There is a fast time scale and a slow time scale in the algorithm. Two simulation studies of constant aging factor and time-varying aging factor are investigated in the simulation environment of MATLAB/SIMULINK. The simulation results indicate that the proposed dual time-scale observer works well under different conditions and the calculational load is reduced significantly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".