Detailed validation of an automotive catalysis model using spatially resolved measurements within the catalyst substrate
Bibliographic record
Abstract
Many kinetic models have appeared in literature in past decades using two main approaches: the traditional global kinetics approach, or the more complex micro‐kinetics approach. Whether global or micro‐kinetics, kinetic models have been based on experimental data obtained at the end of the monolith. The experimental procedure using end pipe analysis may give an accurate overview of the reaction mechanisms that occur; however, the lack of information from within the catalyst can ultimately lead to inaccuracies in the kinetic model and parameters used. Using SpaciMS, a spatially resolved experimental technique developed at the Queen's University Belfast, information from within the catalyst can be obtained. This minimally invasive technique provides detailed information of the gas concentration and temperature profile from inside the catalytic monolith. This paper presents a kinetic model and simulations validated against experimental data obtained from three positions inside the catalyst monolith at 2, 14, and 26 mm in, using data from the SpaciMS. Also, simulations of end pipe analysis, using a commercial reactor, for the CO oxidation are presented and analyzed. The simulations presented are for varying concentrations of both CO and O2 (0.5 % and 1 % CO, 0.5 % and 2 % O2) for both the global and micro‐kinetic approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".