Integrated LCA and Eco-design Process for Hydrogen Technologies: Case Study of the Solid Oxide Electrolyser
Bibliographic record
Abstract
The Life Cycle Assessment (LCA) of a solid oxide electrolyser (SOE) has been performed using publicly available data. The system for producing 1 kg of hydrogen at 25bar and 99.9% purity is represented by a modular structure, which includes the 20-kW solid oxide stack manufacturing, balance of plant equipment, operation consumables, and end-of-life processes. A parametrized life cycle inventory modeling approach was developed. The results illustrate that SOE performs better than steam methane reforming only if supplied by electricity from renewable or nuclear sources. The operation consumables have been identified as the most contributive life stage (67%-89% of potential impacts), followed by equipment manufacturing (7%-22%) and stack manufacturing (4%-11%). Considering the predominant contribution of electricity supply in the consumables, no compromise should be made on ensuring clean electricity sourcing and on the stack energy conversion efficiency. The lifetime of the stack and the balance of plant equipment, as well as the heat mix have been identified as sensitive parameters to minimize the environmental impact of the hydrogen technology. These LCA results have been used to produce a tailored eco-design process for hydrogen projects: (i) organization of an eco-design workshop to present LCA results & environmental hotspots and related key parameters where to leverage eco-design innovations through an open discussion (brainstorm); (ii) provide an eco-design tool obtained from a simplified version of the LCA model parametrized around a limited number key life cycle inventory parameters, enabling the designers/developers to independently test the environmental performance of their innovations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".