Sensitivity analysis and stochastic optimization of levelized cost of hydrogen production through methane pyrolysis
Bibliographic record
Abstract
Abstract Hydrogen plays a crucial role across multiple industrial sectors and is increasingly recognized as a clean energy carrier with significant potential in decarbonization efforts. Methane pyrolysis (MP), particularly using molten metal catalysts, offers a promising pathway for hydrogen production with low CO 2 emissions. However, uncertainties in catalyst performance and market dynamics pose challenges to its economic viability. This study develops an optimization‐based framework to evaluate the levelized cost of hydrogen (LCOH) from MP under techno‐economic uncertainty. A first‐principles process model is integrated into a deterministic optimization formulation to minimize LCOH, followed by sensitivity analysis to identify key cost drivers. The most influential parameters—catalyst loss and natural gas price—are then modelled as uncertain parameters in a two‐stage stochastic programming framework. Results show that the LCOH from MP varies widely, ranging from 0.44 USD/kg under favourable conditions to 21.49 USD/kg in the worst case. The expected LCOH under uncertainty is approximately 7.1 USD/kg, with catalyst degradation emerging as the dominant cost factor. The findings highlight the importance of technological advancements in catalyst stability and carbon removal from molten metals in improving the economic competitiveness of MP.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".