Optimal design of hierarchically structured porous catalysts for autothermal reforming
Bibliographic record
Abstract
A general methodology was presented for the optimization of the macropore network in porous catalysts with given intrinsic kinetics and given nanostructure. Macropores were introduced to reduce diffusion limitations. Macroporosity, macropore size (d), and the thickness of the nanoporous, catalytically active macropore walls (w) influenced the overall product distribution. The optimization was based on a reduced gradient method in combination with a multigrid method to solve the set of discretized partial differential equations representing diffusion and reaction in the hierarchically structured porous material, containing nanoporous catalyst and macropores. The conversion of a single reaction was maximized if w was sufficiently small, and d was constant throughout. This optimum corresponded to a situation where diffusion limitations inside the nanoporous walls of thickness ware avoided, so that the diffusion resistance is limited to the macropores only. In situations with multiple reactions, the optimization methodology might be used to obtain the catalyst structure that maximizes selectivity toward a particular product. This methodology was applied to the important problem of autothermal reforming of natural gas, for the on-board production of hydrogen gas for fuel cells. This is an abstract of a paper presented at the 8th World Congress of Chemical Engineering (Montréal, Quebec, Canada 8/23-27/2009).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".