Life cycle assessment of a newly designed thermochemical cycle and traditional steam methane reforming process for hydrogen production using coherent criteria
Bibliographic record
Abstract
Methodological inconsistencies affect the accuracy and reliability of life-cycle assessment results of hydrogen production and mislead their interpretation. To avoid such consequences, the present work aims to employ newly developed and more coherent life cycle indicators to assess the environmental impacts of renewable hydrogen production. Two case studies are, in this regard, chosen to conduct a comparative assessment between the improved Sulfur–Iodine (S-I) thermochemical cycle and conventional steam methane reforming (SMR). A life cycle assessment (LCA) methodological framework is developed for the first time by coupling the LCA capabilities of the OpenLCA with the process simulation results from the Aspen Plus and thermal management results from the MATLAB. The environmental profile of both hydrogen production systems is assessed using a well-established set of life cycle performance indicators, based on the methodologies, namely: (1) carbon footprint via IPCC, (2) acidification footprint via CML, (3) non-renewable energy footprint via VDI, and (4) non-renewable exergy footprint via VDI. The results show that the predicted carbon footprint of the improved S–I cycle is 1422.71 g CO 2 eq./kg H 2 , whereas the conventional SMR hydrogen production process is associated with a significantly higher carbon footprint of 2642.72 g CO 2 eq./kg H 2 . Furthermore, the S–I cycle has an acidification footprint of 16.18 g SO 2 -eq/kg H 2 , a non-renewable energy footprint of 62.96 MJ eq/kg H 2 , and a non-renewable exergy footprint of 62.09 MJ eq/kg H 2 which is 63.89 %, 12.43 %, and 13.77 % less compared to the conventional SMR process. The environmental performance shows that the improved S–I configuration caused 46.16 % less carbon footprint than the conventional SMR process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".