Comparison of conventional and simplified heterogeneous modeling frameworks for simulation of sulfur poisoning in methane reforming catalyst
Bibliographic record
Abstract
Hydrogen production from methane and carbon dioxide offers a promising route to add value and mitigate climate change. These gases often contain hydrogen sulfide, a well-known catalyst poison, driving the development of sulfur-tolerant catalysts. However, sulfur poisoning has received limited attention in fixed-bed reactor modeling. In this study, two modeling frameworks—simplified and conventional heterogeneous—are developed and compared. The conventional model explicitly accounts for reaction and heat and mass transfer within the catalyst pellet, while the simplified model represents these effects using a catalyst effectiveness factor. Both models are discretized using the finite volume method and programmed in MATLAB, with predictions validated against experimental data from the literature. Kinetic modeling identifies activation energy corrections of 24.4 k J / m o l and 27.0 k J / m o l for the simplified and conventional models, respectively. Transport limitations appear above 1173 K . The order of deactivation was determined to be n = 1.0 , with an average absolute error of 27.2% and 26.2% for methane conversion predictions in simplified and conventional models, respectively, contrasting the more commonly assumed n = 3.0 . Under industrial conditions, both models performed similarly when unpoisoned. However, the conventional model showed an increase in catalyst effectiveness as poisoning occurred, reflecting the slower reaction kinetics relative to mass transport. When the effectiveness in the simplified model was adjusted to match the conventional model, their results realigned. While conventional modeling is more robust, it has a higher computational cost. Simplified modeling remains desirable for assessing catalyst poisoning, but further research is needed to determine how it can account for changes in catalyst effectiveness during poisoning. • Compare simplified and conventional models for reactor during sulfur poisoning in reforming. • Conventional model shows n = 1.0 deactivation order, matching simplified model, unlike literature. • Comparison shows need to update factors in simplified model during catalyst deactivation. • Conventional model is robust, but simplified model is preferred for lower computational cost.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".