An eco-technoeconomic analysis of hydrogen production using solid oxide electrolysis cells that accounts for long-term degradation
Bibliographic record
Abstract
This paper presents an eco-technoeconomic analysis (eTEA) of hydrogen production via solid oxide electrolysis cells (SOECs) aimed at identifying the economically optimal size and operating trajectories for these cells. Notably, degradation effects were accounted by employing a data-driven degradation-based model previously developed by our group for the analysis of SOECs. This model enabled the identification of the optimal trajectories under which SOECs can be economically operated over extended periods of time, with reduced degradation rate. The findings indicated that the levelized cost of hydrogen (LCOH) produced by SOECs (ranging from 2.78 to 11.67 $/kg H 2 ) is higher compared to gray hydrogen generated via steam methane reforming (SMR) (varying from 1.03 to 2.16 $ per kg H 2 ), which is currently the dominant commercial process for large-scale hydrogen production. Additionally, SOECs generally had lower life cycle CO 2 emissions per kilogram of produced hydrogen (from 1.62 to 3.6 kg CO 2 per kg H 2 ) compared to SMR (10.72–15.86 kg CO 2 per kg H 2 ). However, SOEC life cycle CO 2 emissions are highly dependent on the CO 2 emissions produced by its power source, as SOECs powered by high-CO 2 -emission sources can produce as much as 32.22 kg CO 2 per kg H 2 . Finally, the findings of a sensitivity analysis indicated that the price of electricity has a greater influence on the LCOH than the capital 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".