Partial regeneration of Ni-based catalysts for hydrogen production via methane cracking part II: modeling and optimization
Bibliographic record
Abstract
High purity, carbon monoxide-free hydrogen and filamentous carbon can be produced by thermo-catalytic cracking of methane. Carbon filaments continue to grow until the catalyst deactivates because of carbon encapsulation. Regeneration of catalyst is important to maintain a continuous process. Our work on optimization of the partial regeneration method showed that activity of the catalyst can be sustained for longer times by gasifying not all but some extent of the deposited carbon. In the previous work, a kinetic model was developed to be able to understand the reaction mechanism of deactivation of fresh catalyst at the molecular level. The objective of this study was to develop a well-fitted kinetic model for deactivation of regenerated catalyst. A kinetic model that consists of surface reactions, filament formation, and deactivation was developed for the regenerated catalyst. It is assumed that reaction parameters at the molecular level do not change when the burn off degree is at a moderate extent but these parameters changed drastically when burn off degree is vastly increased or decreased. Rate constant for the encapsulation reaction is adjusted for the simulation results to be representative of the experimental results (typically specific weight of carbon (g C/g Ni) vs. time on stream). The system of differential algebraic equations consists of the steady-state equations for all surface intermediates, an algebraic equation of dissolution/segregation, diffusion equation, and the site balance equation. The system has been solved without assuming any rate-determining step or most abundant surface intermediates. Parameter estimation procedures were repeated for the deactivation cycles of regenerated 5 wt% Ni/\gamma-Al_2O_3 catalyst. The basic idea that underlies the model is that every carbon atom will diffuse through the nickel particle and participate in the formation of carbon filaments until the catalyst deactivates. Specific weight of carbon is calculated using the rate of carbon diffusion.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".