A comparative techno-economic assessment between solar-based hydrogen production by methane pyrolysis and water electrolysis methods
Bibliographic record
Abstract
The transition towards clean and economically viable hydrogen production is crucial for ensuring energy sustainability and mitigating climate change . This transition can be effectively facilitated by using renewable energy sources and advanced hydrogen production methods. Methane pyrolysis and water electrolysis emerge as crucial techniques for achieving hydrogen production with minimal carbon intensity. Recognizing the unique opportunity presented by solar energy for both processes, this study presents a comparative techno-economic analysis between solar-based molten salt methane pyrolysis (SMSMP) and solar-based solid oxide electrolyzer cell (SSOEC). This study offers a guideline for selecting SMSMP vs SSOEC for cities across theworld. In particular, a comprehensive case study including five cities worldwide—San Antonio, Edmonton, Auckland, Seville, and Lyon—is conducted, utilizing their dynamic solar data and localized prices of methane and electricity to provide a realistic comparison. The results indicate the superior economic feasibility of SMSMP across all case studies. Among different case studies, San Antonio and Auckland have the lowest hydrogen costs for SMSMP (2.31 $/kgH 2 ) and SSOEC (5.19 $/kgH 2 ), respectively. It was also concluded that SMSMP is preferred over SSOEC in average to ideal solar conditions, given its full dependency on solar thermal energy . However, the SSOEC has the potential to achieve better economic feasibility by incorporating clean hydrogen tax incentives and reducing the costs associated with renewable energy infrastructure in the future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".